JP7023821B2 - Information retrieval system - Google Patents

Description

The present invention relates to an information retrieval system, and more particularly to an information retrieval system that searches for a search target representing a specific point based on a search phrase.

Conventionally, in map search, an information retrieval device capable of performing a map search not only for data in which coordinate information is explicitly registered but also for information included in the contents of atypical data has been known (Patent Documents). 1).

In the information retrieval device of Patent Document 1, data including a plurality of words having an attribute is input, a plurality of words included in the input data are extracted, and a plurality of words are extracted for each attribute, and attribute-related words are converted. The search is performed using the data to which the attribute information is added.

In the information retrieval device of Patent Document 1, for example, in the case of the word "Hokkaido" regarding the attribute of the position, the coordinate information of "Hokkaido" is added to the atypical data, so that it is based on the search keyword input by the user. It is said that a predetermined search result can be output.

Japanese Unexamined Patent Publication No. 2014-191714

However, in the configuration of the information retrieval device of Patent Document 1, the search result cannot be output unless the word has an attribute, and a more accurate search target cannot be detected from the search phrase.

An object of the present invention is to detect a search target with higher accuracy.

The information retrieval system according to the first aspect of the present disclosure includes an input device, a feature word generation unit, a search unit, and a display device. A search phrase is input to the above input device. The feature word generation unit generates a feature word from a search phrase. The above characteristic words are related to words representing search targets. The search unit searches the storage database for the search target. The storage database stores the feature words and the search target. The search target corresponds to the feature word. The display device displays the search target. The search unit searches for the search target corresponding to the feature word based on the feature word.

The information retrieval system of the first aspect can detect a search target with higher accuracy.

According to the present invention, it is possible to detect a search target with higher accuracy.

It is a schematic block diagram of the information retrieval system which concerns on this embodiment. It is a flowchart which shows the processing operation of the information retrieval system which concerns on this embodiment. It is a flowchart which shows an example of the process of searching a spot based on a cluster. It is a figure which shows the specific example which determined the cluster from the search phrase. It is a figure which shows an example of the data table stored in a category database. It is a figure which shows an example of the data table stored in a spot database. It is a block diagram which shows an example of the main part structure of an information storage apparatus. It is a flowchart which shows the example of the process which generates a spot database. It is a flowchart which shows the example of the process which generates a category database. It is a block diagram which shows the deformation of the main part composition of an information storage device. It is a flowchart which shows another example of the process which generates a storage database. It is a schematic diagram in which a search example is displayed on the display device of an information processing terminal.

Hereinafter, the information retrieval system 10 of the present embodiment will be described. As shown in FIG. 1, the information retrieval system 10 includes an information processing terminal 1, an information retrieval device 2, and an information storage device 3. The information retrieval device 2 is configured to be able to communicate with the information processing terminal 1. The information storage device 3 is configured so that the information retrieval device 2 can access it.

The information processing terminal 1 includes an input device 11, a terminal communication device 12, and a display device 13. The input device 11 is configured so that a search phrase can be input. The terminal communication device 12 is configured so that the search phrase can be transmitted to the information retrieval device 2. The terminal communication device 12 is configured to be able to receive the search target searched based on the search phrase from the information search device 2. The display device 13 is configured to be able to display the search target.

The information retrieval device 2 includes a search communication device 21, a position information acquisition unit 22, a feature word generation unit 24, and a search unit 25. The search communication device 21 is configured to be able to receive a search phrase from the information processing terminal 1. The search communication device 21 is configured to be able to transmit a search target. The position information acquisition unit 22 is configured to be able to acquire position information. The location information indicates the location on the map that is the starting point of the search. The feature word generation unit 24 generates a feature word from a search phrase. Characteristic words are associated with words that represent the search target. The search unit 25 is configured to be able to search for a search target from the storage database.

The search target has point information. The point information indicates a specific point. The search unit 25 is configured to be able to search for a search target corresponding to a feature word within a predetermined range from the position based on the position information and the feature word.

The information storage device 3 includes a storage storage device 31. The storage storage device 31 stores the storage database. The storage database stores feature words and search targets. The search target corresponds to a characteristic word.

The information retrieval system 10 of the present embodiment can detect a search target with higher accuracy.

Hereinafter, each configuration in the information retrieval system 10 of the present embodiment will be specifically described.

The information retrieval system 10 according to the present embodiment is configured so that the information processing terminal 1 used by the user and the information retrieval device 2 can communicate with each other. The information processing terminal 1 and the information retrieval device 2 are connected by wireless communication, for example, via a public communication network 50. In FIG. 1, a double-headed arrow illustrates the flow of information in which communication is performed via the public communication network 50.

As shown in FIG. 1, the information processing terminal 1 includes a terminal control device 14 and a terminal storage device 15 in addition to an input device 11, a terminal communication device 12, and a display device 13. The information processing terminal 1 is a terminal device used by the user. Examples of the terminal device include a mobile phone or a smartphone.

The input device 11 is configured so that a search phrase can be input. A search phrase is a word for searching a search target. The search phrase is also a word arbitrarily entered by the user. The search phrase may be a word, a combination of words, or a sentence containing a subject and a predicate. The input device 11 is configured so that the input search phrase can be instructed to be transmitted from the terminal communication device 12. Examples of the input device 11 include a touch panel integrally configured with the display device 13, a voice input device capable of inputting by voice, or a physical key. The input device 11 is not limited to the configuration of the touch panel integrally configured with the display device 13, and may be simply configured with the input unit.

The terminal communication device 12 is configured to be able to communicate with the information retrieval device 2. The terminal communication device 12 transmits the information of the search phrase. The terminal communication device 12 receives the information to be searched. Since the search target has information indicating a specific point, it is also point information. The point information is POI (Point Of Interest) information. Specifically, the terminal communication device 12 is configured to be able to communicate with the search communication device 21 of the information retrieval device 2 by using the public communication network 50. The terminal communication device 12 may be configured to enable not only public communication using the public communication network 50 but also wired communication, for example. Further, the terminal communication device 12 may be configured to perform wireless communication corresponding to infrared communication, Wi-Fi (registered trademark), Bluetooth (registered trademark), or BLE (Bluetooth Low Energy) communication standard.

The display device 13 is configured to be able to display various types of information. The display device 13 displays, for example, a search phrase. The display device 13 may display a map. The display device 13 may display the points indicated by the point information of the search target searched by the information search device 2 on the map. For example, the display device 13 can visualize a plurality of point information and display it on the map indicated by the map information stored in the terminal storage device 15. The display device 13 is not limited to the configuration of the touch panel integrally configured with the input device 11, but may be simply the configuration of the display unit.

The terminal control device 14 controls various information processing operations. The terminal control device 14 is electrically connected to the input device 11, the terminal communication device 12, the display device 13, and the terminal storage device 15. The terminal control device 14 is configured to be able to control the input device 11, the terminal communication device 12, and the display device 13 based on the program stored in the terminal storage device 15. The terminal control device 14 is composed of, for example, a CPU (Central Processing Unit) or an MPU (Micro Processing Unit).

The terminal storage device 15 has a volatile storage device in which information disappears when the power is turned off, and a non-volatile storage device in which information does not disappear even when the power is turned off. The volatile storage device temporarily stores the information processed by the terminal control device 14. The volatile storage device stores, for example, map information indicating a map and point information added to the map information. Examples of the volatile storage device include RAM (Random Access Memory). The non-volatile storage device is a storage that can store various information processing programs and various information. Examples of the non-volatile storage device include ROM (Read Only Memory). As the ROM, for example, a flash memory or an HDD (Hard Disk Drive) is used.

As shown in FIG. 1, the information retrieval device 2 includes a search communication device 21 and a search control device 27. The information retrieval device 2 includes a search storage device 28 in addition to the search communication device 21 and the search control device 27. The information retrieval device 2 is composed of, for example, a server. The search control device 27 includes a position information acquisition unit 22, a category determination unit 23, a feature word generation unit 24, a search unit 25, and a timekeeping unit 26.

The search communication device 21 is configured to be able to communicate with the information processing terminal 1. The search communication device 21 is configured to receive information on the search phrase from the information processing terminal 1. The search communication device 21 is configured to be able to transmit the information of the search target searched by the search control device 27 to the information processing terminal 1. That is, the search communication device 21 receives the information of the search phrase. The search communication device 21 transmits information to be searched. Specifically, the search communication device 21 is configured to be able to communicate with the terminal communication device 12 of the information processing terminal 1 by using the public communication network 50.

Further, the search communication device 21 is configured to be able to communicate with the information storage device 3. The search communication device 21 transmits information for searching the search target. The search communication device 21 receives the searched information to be searched from the information storage device 3. Specifically, the search communication device 21 is configured to be able to communicate with the storage communication device 33 of the information storage device 3 by using the public communication network 50. The search communication device 21 may be configured to enable not only public communication using the public communication network 50 but also wired communication, for example. Further, the terminal communication device 12 may be configured to perform infrared communication, Wi-Fi (registered trademark), Bluetooth (registered trademark), or wireless communication corresponding to the communication standard of BLE.

The search control device 27 is electrically connected to the search communication device 21 and the search storage device 28. The search control device 27 is configured so that the position information acquisition unit 22, the category determination unit 23, the feature word generation unit 24, the search unit 25, and the timekeeping unit 26 function. The search control device 27 drives, for example, to operate a position information acquisition program, a category determination program, a feature word generation program, a search program, and a timekeeping program stored in the search storage device 28. The search control device 27 is composed of, for example, a CPU or an MPU.

The position information acquisition program functions as a position information acquisition unit 22 for acquiring position information by being executed on a computer. The category discriminating program functions as a category discriminating unit 23 for discriminating the category of the search phrase by executing it on a computer. The feature word generation program functions as a feature word generation unit 24 that generates a feature word from a search phrase by being executed on a computer. The search program functions as a search unit 25 for searching a search target from the storage database stored in the information storage device 3 by being executed on a computer. The timekeeping program functions as a timekeeping unit 26 capable of measuring year, month, day, and hour by being executed on a computer.

The position information acquisition unit 22 acquires the position information. The position information is, for example, longitude information and latitude information. The position information may include altitude information in addition to longitude information and latitude information. The position information acquisition unit 22 can acquire the position information transmitted from the information processing terminal 1, for example. Examples of the position information transmitted from the information processing terminal 1 include the position information input by the input device 11 of the information processing terminal 1. Further, the position information acquisition unit 22 can acquire the position information from the search phrase transmitted from the information processing terminal 1.

The search phrase is composed of text data. The search phrase contains the phrase. The phrase may be one word or a collection of a plurality of words. The phrase may be written. The search phrase is converted into a format that can handle the text data mechanically. The search phrase is decomposed into a plurality of words by morphological analysis. Here, morphological analysis can classify search phrases into the smallest units that have meaning in natural language. In the morphological analysis, it is also possible to acquire the part-of-speech information of the classified morphemes. In the morphological analysis, for example, it is possible to acquire information on whether or not the word included in the search phrase is a noun, an adjective, a verb, or an adverb.

For morphological analysis, for example, a morphological analysis engine such as MeCab can be used. MeCab can be used with a named entity dictionary. Examples of named entity dictionaries include system dictionaries such as Neologd. The position information acquisition unit 22 performs morphological analysis of the search phrase in order to obtain a word included in the search phrase. The position information acquisition unit 22 is not limited to a configuration in which the search phrase is morphologically analyzed in order to obtain a word included in the search phrase. The position information acquisition unit 22 may be configured such that, for example, a category discrimination unit 23, a feature word generation unit 24, a search unit 25, or a morphological analysis unit (not shown) obtains a word obtained by morphologically analyzing a search phrase. The position information acquisition unit 22 may perform a distributed expression in which the morphologically analyzed word is expressed numerically. The position information acquisition unit 22 may summarize the inflected forms of the words analyzed by morphological analysis. In order to summarize the inflected forms of the words analyzed by morphological analysis, for example, FastText may be used.

When the word included in the search phrase is a place name, the position information acquisition unit 22 can acquire the position information from the place name database in which the place name and the position information correspond to each other. The position information acquisition unit 22 compares, for example, a word obtained by decomposing a search phrase by morphological analysis with a place name database stored in the search storage device 28. If the word obtained by decomposing the search phrase by morphological analysis matches the place name in the place name database, the position information acquisition unit 22 may acquire the position information corresponding to the matched place name.

The place name database may be configured, for example, by associating the names of prefectures, cities, wards, towns, villages or facilities with the location information. Here, the place name includes not only the name representing the city, ward, town, or village, but also the name of the facility. In other words, a place name is a name that indicates geographical information. In the place name database, for example, the name of the prefecture and the location information of the prefectural office, the name of the city and the location information of the city hall, the name of the ward and the location information of the ward office, the name of the town and the location information of the town hall, the name of the village and the village office. It is configured so that the location information, the name of the facility and the location information of the facility correspond to each other. In the following, when the word included in the search phrase is a place name, acquiring the location information from the place name database in which the place name and the location information directly correspond to each other is also referred to as acquisition of the location information by non-machine learning. When the position information acquisition unit 22 acquires the position information by non-machine learning, the position information acquisition unit 22 performs a hard search as to whether or not the word matches the word included in the database.

When the word included in the search phrase does not include the place name, the location information acquisition unit 22 can acquire the location information from the database in which the search term and the location information are associated with each other by machine learning. The position information acquisition unit 22 replaces the search phrase with a word decomposed by morphological analysis and a search word having a high degree of similarity by, for example, machine learning. The position information acquisition unit 22 can, for example, perform morphological analysis on the search phrase and select a desired search term based on the degree of similarity between the pre-stored word and the search term. The location information acquisition unit 22 compares with a place name database in which the search term and the location information correspond to each other. If the search term matches the place name in the place name database, the position information acquisition unit 22 may acquire the position information corresponding to the matched place name. In other words, the position information acquisition unit 22 uses a geocoding technique for converting a place name indicating geographical information into coordinate values of longitude and latitude.

The location information acquisition unit 22 uses at least one of the location information from the information processing terminal 1, the word of the place name included in the search phrase from the information processing terminal 1, and the search term based on the search phrase from the information processing terminal 1. Then, the position information indicating the position on the map can be acquired. The position information acquisition unit 22 acquires the position information indicating the position on the map by using the information from the information processing terminal 1. In the information retrieval device 2 according to the present embodiment, even if the position information cannot be directly acquired from the information processing terminal 1, the location information can be indirectly acquired. The information retrieval device 2 can acquire position information with a lower processing load as compared with a configuration in which position information is acquired only by machine learning.

That is, in the information retrieval device 2 according to the present embodiment, the position information acquisition unit 22 acquires the position information. The position information is estimated from the words included in the search phrase by machine learning.

The information retrieval device 2 according to the present embodiment can indirectly estimate the position information from the words included in the search phrase by machine learning even if the word indicating the position information is not directly included in the search phrase. can.

The category determination unit 23 is configured to be able to determine the category of the search phrase. The category determination unit 23 determines the category of the search phrase transmitted from the information processing terminal 1. Categories include, for example, hot springs, parks, museums, shrines, live concerts, shopping, amusement, the outdoors, events, gourmet food, ramen, and mountains. The categories are divided into appropriate types. The types of categories can be, for example, 300 types.

The category determination unit 23 performs morphological analysis of the search phrase. The category determination unit 23 is not limited to a configuration that morphologically analyzes the search phrase in order to obtain a word included in the search phrase. The category discrimination unit 23 may be configured such that, for example, a position information acquisition unit 22, a feature word generation unit 24, a search unit 25, or a morphological analysis unit (not shown) obtains a word obtained by morphologically analyzing a search phrase. The category discriminating unit 23 may perform a distributed expression in which the word analyzed by the morphological element is expressed numerically. The category discriminating unit 23 may summarize the inflected forms of the words analyzed by morphological analysis.

When the word included in the search phrase is a category name representing a category, the category determination unit 23 can directly determine the category from the category database in which the category name and the word correspond to each other. The category determination unit 23 compares, for example, a word obtained by decomposing a search phrase by morphological analysis with a category database stored in the search storage device 28. If the word decomposed by the morphological analysis of the search phrase matches the category name of the category database, the category determination unit 23 may store the matched category name in the search storage device 28 in association with the search phrase. In the following, when the word included in the search phrase is a category name, determining the category from the category database is also referred to as category determination. That is, it can be said that the category determination unit 23 includes a category determination unit 231 that determines the category from the category database by non-machine learning when the word included in the search phrase is the category name by the category determination.

When the word included in the search phrase does not include the category name, the category discriminating unit 23 can estimate the category from the category database in which the search term and the category name are associated with each other by machine learning. For example, the category discriminating unit 23 replaces a word obtained by decomposing a search phrase by morphological analysis by machine learning with a search word having a high degree of similarity to the word. In this case, the category discriminating unit 23 may replace one word with a search word having a high degree of similarity to the word among the words decomposed by the morphological analysis of the search phrase, or replace two or more words with those words. It may be replaced with a search term having a high degree of similarity with. Further, the category discriminating unit 23 may, for example, extract co-occurrence words from two or more words and replace the co-occurrence words with words having a high degree of similarity to words clustered in advance for each category. .. Here, co-occurrence means that a predetermined word included in a search phrase appears at the same time as another word in a search phrase or the like. A co-occurrence word is another word that appears at the same time as a predetermined word. Since the words used in the categories are often different depending on the classification, it is preferable that the category discrimination unit 23 performs category discrimination by machine learning using teacher data in which the input and the corresponding output are known in advance.

The category discriminating unit 23 can, for example, perform morphological analysis on the search phrase and select a desired search word based on the degree of similarity between the pre-stored word and the co-occurrence word. The desired search term is set to match the category name. The category determination unit 23 compares the search term matching the category name with the category database. If the search term matches the category name of the category database, the category determination unit 23 may presume that the matched category name is the category of the search phrase. That is, the category discrimination unit 23 includes a category estimation unit 232 that estimates the category from the category database by machine learning when the search term similar to the word included in the search phrase is the category name by the category estimation. I can say.

The category determination unit 23 determines a category by using a word indicating a category included in the search phrase from the information processing terminal 1 or a search term based on the word included in the search phrase from the information processing terminal 1. Can be done.

In the information retrieval device 2 according to the present embodiment, even if the category cannot be directly determined from the search phrase, the category can be indirectly determined based on the word included in the search phrase. When the information retrieval device 2 estimates a category by machine learning, it can determine the category more accurately than when it determines the category by non-machine learning.

In other words, the information retrieval device 2 according to the present embodiment further includes a category determination unit 23 and a search storage device 28. The category determination unit 23 determines the category of the search phrase. The search storage device 28 stores words corresponding to the categories. The category determination unit 23 has a category determination unit 231 and a category estimation unit 232. The category determination unit 231 determines the category depending on whether or not the word included in the search phrase matches the word stored in the search storage device 28 in advance. The category estimation unit 232 estimates the category from the words included in the search phrase not determined by the category determination unit 231 by machine learning.

The information retrieval device 2 according to the present embodiment can indirectly estimate the category from the words included in the search phrase by machine learning even if the word indicating the category is not directly included in the search phrase. Since the information retrieval device 2 has the category determination unit 231 and the category estimation unit 232, the category can be determined with a lower processing load as compared with the configuration in which the category is determined only by machine learning.

The feature word generation unit 24 is configured to be able to generate a feature word related to a word representing a search target. The feature word generation unit 24 generates a feature word from a search phrase transmitted from the information processing terminal 1. The feature word generation unit 24 morphologically analyzes the search phrase in order to generate the feature word from the search phrase. The feature word generation unit 24, for example, obtains a word obtained by morphological analysis of a search phrase by a position information acquisition unit 22, a category determination unit 23, a search unit 25, or a morphological analysis unit (not shown), and generates a feature word. It may be configured. The feature word generation unit 24 performs a distributed expression in which the morphologically analyzed word is expressed numerically. The feature word generation unit 24 summarizes the inflected forms of the words analyzed by morphological analysis. The above-mentioned "characteristic word" may be one word or a group consisting of a plurality of words. In addition, "generation of a feature word" includes determining a feature word or selecting a feature word.

The feature word generation unit 24 replaces a word obtained by decomposing a search phrase by morphological analysis with a feature word having a high degree of similarity to the word, for example, by machine learning. The feature word generation unit 24 may replace one word with a feature word having a high degree of similarity among the words decomposed by the morphological analysis of the search phrase, or replace two or more words with the feature word having a high degree of similarity to those words. It may be replaced with a high feature word. The feature word generation unit 24 generates a feature word having a higher degree of similarity to the word representing the search target from the words included in the search phrase by machine learning. The feature word generation unit 24 can select a desired feature word based on the degree of similarity between the word stored in advance and the feature word, for example, after performing morphological analysis on the search phrase. More specifically, the feature word generation unit 24 can determine how far apart by measuring the feature vector of the feature word with a cosine similarity. That is, the feature word generation unit 24 can determine the degree of similarity by measuring the feature vector of the feature word with cosine similarity.

The feature vector can be obtained by an algorithm such as word2vec, for example, using data in which a word analyzed by morphological analysis is expressed numerically as input data. Algorithms such as word2vec used here are said to have been machine-learned so that the feature vector of the input word can be output. For cosine similarity, the cosine of two vectors is calculated, and the value of the cosine is used as the degree of similarity. Cosine similarity represents the closeness of the angles formed by the vectors, and the closer it is to 1, the more similar it is. Cosine similarity means that the closer it is to 0, the less similar it is. The feature word generation unit 24 may calculate statistical data of search phrases according to the degree of similarity. It is preferable that the feature word generation unit 24 is configured to preferentially output a feature word having a higher frequency based on statistical data among a plurality of feature words.

The feature word generation unit 24 extracts co-occurrence words from the words included in the search phrase. In this case, the feature word generation unit 24 may use a co-occurrence word as the feature word, and the search unit 25 may search the search target from the feature word. By using the co-occurrence word by the feature word generation unit 24, it is possible to effectively derive the search target even if the word does not have a correlation, which seems to be meaningless to represent the search target. In other words, the feature word generation unit 24 generates a feature word, which is a feature vector that more easily represents a search target, from the feature vector by machine learning.

That is, the feature word generation unit 24 generates a feature word related to the word representing the search target from the search phrase. In the information retrieval device 2 according to the present embodiment, the feature word generation unit 24 generates a feature word having a higher degree of similarity to the word representing the search target from the word including the noun, the adjective, the verb, and the adverb in the search phrase. do.

The information retrieval device 2 can detect a search target with higher accuracy by generating a feature word having a higher degree of similarity to a word representing the search target. In particular, when the word included in the search phrase is an adjective, the information retrieval device 2 generates a characteristic word from at least the word containing the adjective, so that it is possible to search for a search target having a subjective or emotional meaning. ..

The search unit 25 is configured to be able to search for a search target from the storage database. In the storage database, characteristic words are classified by category. The search target has point information indicating a specific point. The specific point represents the position of the search target on the map. The point information is, for example, longitude information and latitude information. The point information may include altitude information in addition to longitude information and latitude information. The search unit 25 acquires location information from the location information acquisition unit 22. The search unit 25 acquires category information from the category determination unit 23. The search unit 25 acquires a feature word from the feature word generation unit 24. The search unit 25 searches the storage database for the search target based on the location information, the category, and the feature word. The search target searched by the search unit 25 corresponds to a feature word in a predetermined range from the position indicated by the position information and in a category determined. In other words, the search unit 25 searches for a search target corresponding to the feature word in a predetermined range from the position indicated by the position information and the category determined based on the position information, the category, and the feature word. It is configured to be able to.

The timekeeping unit 26 is configured to be able to time the year, day, and hour. The timekeeping unit 26 may be configured so that it can be timed by, for example, counting the number of clocks of the CPU. The timekeeping unit 26 can specify, for example, the date and time and the time when the search communication device 21 receives the search phrase from the information processing terminal 1. The timekeeping unit 26 can discriminate the search phrase received by the search communication device 21 at predetermined intervals. The timekeeping unit 26 can store the date and time and the time when the search phrase is received in the search storage device 28 in association with the statistical data.

The search storage device 28 has a volatile storage device in which information disappears when the power is turned off, and a non-volatile storage device in which information does not disappear even when the power is turned off. The volatile storage device temporarily stores the information processed by the search control device 27. An example of a volatile storage device is, for example, RAM.

The non-volatile storage device is a storage that can store a position information acquisition program, a category determination program, a feature word generation program, a search program, and a timekeeping program. An example of a non-volatile storage device is, for example, a ROM. Further, the non-volatile storage device may store a place name database, a category database, statistical data, or a system dictionary.

In the information retrieval system 10 of the present embodiment, for example, when the content of the search phrase is "a restaurant with a beautiful night view", the feature word generation unit 24 uses morphological analysis to "night view", "beautiful", and "restaurant". To get the word. The feature word generation unit 24 determines the similarity of the feature words co-occurring with "night view", "beautiful", or "restaurant" by machine learning, and the search unit 25 stores the database based on the determined feature words. It is possible to search for the location information of a "restaurant" whose search target is ambiguous from.

The information storage device 3 includes a storage control device 32 and a storage communication device 33 in addition to the storage storage device 31. The information storage device 3 is composed of, for example, a database server.

The storage storage device 31 stores a storage database in which search targets searched by the information retrieval device 2 are stored. In the storage database, search targets are classified by category. In the storage database, search targets are stored in correspondence with characteristic words.

The storage storage device 31 has a volatile storage device in which information disappears when the power is turned off, and a non-volatile storage device in which information does not disappear even when the power is turned off. The volatile storage device temporarily stores the information processed by the storage control device 32. Examples of the volatile storage device include RAM. The non-volatile storage device is a storage that can store various information processing programs and various information. Examples of the non-volatile storage device include ROM. The non-volatile storage device stores the storage database. The non-volatile storage device may store map information indicating a map and point information added to the map information.

The storage control device 32 is electrically connected to the storage storage device 31 and the storage communication device 33. The storage control device 32 is configured to be able to control the storage storage device 31 and the storage communication device 33. The storage control device 32 can be configured by using a CPU or an MPU. The storage control device 32 is configured so that, for example, a predetermined program can be implemented based on a program stored in the storage storage device 31 in advance.

The storage communication device 33 is configured so that the information retrieval device 2 can access it. The storage communication device 33 is configured to receive information for searching a search target from the information retrieval device 2. The storage communication device 33 is configured to be able to transmit information to be searched to the information retrieval device 2.

Further, the information storage device 3 may be configured so that a storage database can be constructed from an SNS (Social Networking Service) provided by an external information processing device (not shown). The information storage device 3 constructs a storage database from the generated feature words in the same manner as the information retrieval device 2 generates the feature words from the search phrase based on the posted article information disclosed to the outside by the external information processing device. can do. The posted article information is, for example, information on a posted article posted by a user to an external information processing device.

Next, the operation of the entire information retrieval system 10 according to the present embodiment will be described.

First, the relationship between the information processing terminal 1 and the information retrieval device 2 will be described. In the information retrieval system 10 according to the present embodiment, the user inputs a search phrase by the input device 11 of the information processing terminal 1. When the input device 11 receives the instruction to transmit the search phrase, the information processing terminal 1 transmits the search phrase from the terminal communication device 12 to the information retrieval device 2.

Further, the information processing terminal 1 causes the terminal communication device 12 to transmit the position information indicating the selected position to the information retrieval device 2 by the user selecting the position on the map displayed on the display device 13. It may be configured. When the information retrieval device 2 receives the location information from the information processing terminal 1, the location information can be used as the starting point on the search map.

Next, the operation of the information retrieval device 2 that has received the search phrase from the information processing terminal 1 will be described with reference to FIG. In the following, each step in the figure is indicated by S.

In the flowchart shown in FIG. 2, among the steps performed by the information retrieval device 2, the information retrieval device 2 receives a search phrase from the information processing terminal 1 (S101). The information retrieval device 2 stores the received search phrase in the search storage device 28.

The information retrieval device 2 performs a location information acquisition step of acquiring location information by a location information acquisition program whose operation is controlled by the search control device 27 (S102). The position information acquisition program acquires the position information when the position information is transmitted from the information processing terminal 1 in the position information acquisition process. In the position information acquisition process, the position information acquisition program acquires the position information from the search phrase when there is no position information that can be acquired.

Next, the information retrieval device 2 performs a category discrimination step of discriminating the category of the search phrase by the category discriminating program whose operation is controlled by the search control device 27 (S103). If the search phrase contains a word indicating a place name, the category determination program determines the category name from the category database. If the search phrase does not contain a word indicating a place name, the category discrimination program generates a search term from the search phrase by machine learning. The category discriminant program estimates the category name from the category database based on the search terms having a high degree of similarity among the generated search terms.

Subsequently, the information retrieval device 2 performs a feature word generation step of generating a feature word related to a word representing a search target from a search phrase by a feature word generation program whose operation is controlled by the search control device 27 (S104).

Next, the information retrieval device 2 performs a search execution step of searching the search target from the storage database by the search program whose operation is controlled by the search control device 27 (S105).

Finally, the information retrieval device 2 may be terminated by searching for the search target.

According to the information retrieval system 10 of the present embodiment, even if the user inputs a search phrase based on an ambiguous image into the information processing terminal 1, the information retrieval device 2 performs a soft search using the user's subjective words or emotional words. It can be carried out. The information retrieval device 2 displays, for example, the location of a shop where the spicy ramen may be eaten on the display device 13 on the display device 13 of the information processing terminal 1 even if the search phrase "super spicy ramen" is input. It can be displayed with a marker of the color corresponding to the category of ramen on the map.

That is, in the information retrieval system 10 of the present embodiment, the information processing terminal 1 further includes a terminal storage device 15. Map information is stored in the terminal storage device 15. The display device 13 displays a specific point indicating a search target on a map of map information.

The information retrieval system 10 of the present embodiment can display points to be searched with higher accuracy on a map.

Further, in the information retrieval system 10 of the present embodiment, the feature word generation unit 24 calculates statistical data of the search phrase. The information storage device 3 associates the search phrase received by the search communication device 21 with the statistical data within a preset period from the information processing terminal 1 and stores the statistical data in the storage database.

The information retrieval system 10 of the present embodiment can detect a search target with higher accuracy based on a search phrase within a preset period.

Further, in the information retrieval system 10 of the present embodiment, the feature word generation unit 24 generates a feature word having a higher degree of similarity to the word representing the search target from the words included in the search phrase. The feature word generation unit 24 is configured to preferentially output feature words with higher frequency based on statistical data.

The information retrieval system 10 of the present embodiment can detect a search target with higher accuracy.

In the information retrieval system 10 of the above-described embodiment, for example, in the case of a configuration provided with a touch panel, the information processing terminal 1 is used as an area for searching by enlarging or reducing the map displayed on the display device 13. By tapping the place you want to specify, you can have the input device 11 input the position information.

Further, the information processing terminal 1 displays the search target as a marker on the display device 13 so as to be displayed on the map. Examples of the marker include a balloon in which a pictogram representing a category is displayed in the center. The information processing terminal 1 may display the map including the marker on the display device 13, for example, by changing the color or shading of the marker for each category. For example, the information processing terminal 1 may change the size of the marker according to the degree of similarity between the word included in the search phrase and the feature word, and display the map including the marker on the display device 13.

Further, in the information retrieval system 10 of the above-described embodiment, the information processing terminal 1 may have a navigation function. Since the information processing terminal 1 has a navigation function, the user can be guided to the point information on the map displayed on the display device 13. The navigation function is stored in the terminal storage device 15 of the information processing terminal 1 and is controlled by the terminal control device 14. The navigation function may be stored in the search storage device 28 of the information search device 2 and controlled by the search control device 27. The information retrieval system 10 may be equipped with a navigation function on either the information processing terminal 1 or the information retrieval device 2 as long as the map and navigation can be displayed on the display device 13 of the information processing terminal 1.

Specifically, one of the information retrieval device 2 and the information processing terminal 1 may be configured to store the navigation function, and the other device may control the navigation function. Alternatively, even if the navigation function is divided and stored in both the information retrieval device 2 and the information processing terminal 1, one of the devices or both devices cooperate to control the navigation function. good.

Each process or each function of the above embodiment may be realized by centralized processing by a single device or a single system, or may be distributed processing by a plurality of devices or a plurality of systems. It may be realized by. Further, each component of the above embodiment may be configured by dedicated hardware. Each component of the above embodiment may be realized by executing a program for a component that can be realized by software.

Each component of the above embodiment may be realized, for example, by the CPU executing a software program recorded on a recording medium. The program may be executed by being downloaded from the server, or may be executed by reading the program recorded on a predetermined recording medium. Further, the number of computers that execute the program may be singular or plural. The above embodiment may be configured to perform centralized processing or may be configured to perform distributed processing.

It should be noted that the present invention can be carried out in various other forms without departing from its spirit, gist or main characteristics. The above embodiments are merely exemplary in all respects and should not be construed in a limited way. That is, by taking the above embodiment as an example, the present invention is an information retrieval device or an information retrieval system.

[Summary of Embodiment]

(1: About feature words)
According to the information retrieval device 2 described above, it is assumed that the search phrase input by the user performing the search does not directly indicate the spot that the user wants to search by the function of each part included in the search control device 27. Can also detect valid spots and present them to the user. Here, the spot is also a search target.

Specifically, the search control device 27 includes a feature word generation unit 24 and a search unit 25. Of these, the feature word generation unit 24 generates feature words from words obtained by morphological analysis of the search phrase. Hereinafter, the word obtained by morphological analysis of the search phrase may be referred to as an input word. In this specification, the word "generation" is used to mean "specific", "determination", "detection", or "selection" with respect to the feature word.

Here, the feature word generated by the feature word generation unit 24 may be a similar word having a high degree of similarity to the input word, or may be a co-occurrence word of the input word. The feature word generated by the feature word generation unit 24 may be both a similar word and a co-occurrence word. In the following, a word having a degree of similarity with an input word higher than a predetermined threshold value may be referred to as a similar word.

When a similar word is used as a feature word, the feature word generation unit 24 generates feature information indicating the feature of the input word. The feature information may be any information that indicates the feature of the input word. The feature information may be, for example, a feature vector of an input word. The feature vector can be calculated by using, for example, an algorithm of GloVe, WordNet, or fastText in addition to word2vec. The feature word generation unit 24 calculates the similarity between the feature vector of each feature word associated with the spot in the storage database and the calculated feature vector, and identifies words having a similarity equal to or higher than the threshold value. As a result, the feature word generation unit 24 can identify a similar word similar to the input word among the feature words associated with the spot in the storage database. The method for calculating the degree of similarity is not particularly limited, and can be calculated by, for example, cosine similarity.

When a co-occurrence word is used as a feature word, the feature word generation unit 24 specifies the co-occurrence word of the input word. The co-occurrence word can be extracted by, for example, TF-IDF (Term Frequency-Inverse Document Frequency). Further, the feature word generation unit 24 may use a co-occurrence word of a similar word as the feature word.

Then, the search unit 25 searches the storage database stored in the storage storage device 31 using the generated feature words. The search unit 25 searches the storage database to detect spots associated with feature words in the storage database. The "spot" means, for example, a point or place, or a building installed at a specific point or place. In the storage database, each spot is associated with location information indicating the geographical position of the spot. Since the feature word used for the search is a similar word or a co-occurrence word of the input word, even if the search phrase does not directly indicate the spot that the user wants to search, a valid spot is detected and the user is notified. Can be presented.

In addition, the feature word generation unit 24 may target the word of a predetermined part of speech among the words obtained by morphological analysis of the search phrase as the target of feature word generation. The predetermined part of speech may be, for example, a noun, an adjective, a verb, and an adverb. As a result, it is possible to generate highly valid feature words by excluding parts of speech such as conjunctions, interjections, particles, and auxiliary verbs, which are difficult to generate significant feature words.

(2: About location information)
The search control device 27 may include a position information acquisition unit 22. The position information acquisition unit 22 acquires position information as a reference for determining a spot search range based on the information received from the information processing terminal 1 used by the user. The search unit 25 sets a predetermined range based on the position indicated by the acquired position information, and detects a spot within the set range. As a result, the information retrieval system 10 can search for spots narrowed down to the area of interest to the user performing the search.

The position information acquisition unit 22 may acquire position information from a word obtained by morphological analysis of a search phrase. For example, if the word obtained by morphological analysis of the search phrase includes a word indicating a position, the position information acquisition unit 22 uses a place name database in which the word indicating the position and the position information are associated with each other. You may acquire the location information. In addition, as a word indicating a position, for example, a place name can be mentioned.

Further, the position information acquisition unit 22 may acquire the position information by using the position information database for specifying the position information. The position information database is a database in which a word and position information indicating a position corresponding to the word are associated with each other. When the position information acquisition unit 22 acquires the position information using the position information database, the position information acquisition unit 22 uses at least one of a word obtained by morphological analysis of a search phrase, a word similarity word, and a co-occurrence word of the word. Search the information database. As a result, the position information acquisition unit 22 can acquire the position information related to the word obtained by morphological analysis of the search phrase. It should be noted that similar words and co-occurrence words may be specified by using a machine-learned model. In this case, the position information acquisition unit 22 acquires the position information estimated by machine learning from the words included in the search phrase.

Further, the information processing terminal 1 may acquire position information indicating the position of its own machine by using, for example, GPS (Global Positioning System), and may transmit the acquired position information to the information retrieval device 2. In this case, the position information acquisition unit 22 may acquire the position information using GPS transmitted from the information processing terminal 1. The information retrieval system 10 can also detect spots around the current location of the information processing terminal 1 by using the position information acquired by using GPS.

(3: About categories)
The search control device 27 may include a category determination unit 23. The category determination unit 23 may include a category determination unit 231 and a category estimation unit 232. The category determination unit 23 determines the category of the word obtained by morphological analysis of the search phrase. Further, in this case, in the storage database, each spot is associated with the category of the spot. As a result, the search unit 25 can detect the spots in the category determined by the category determination unit 23 among the spots corresponding to the search phrase.

The category determination unit 231 can determine the category corresponding to the word obtained by morphological analysis of the search phrase by using, for example, a category database in which the word and the category corresponding to the word are associated with each other.

The category estimation unit 232 uses a machine-learned algorithm to identify a category corresponding to a word obtained by morphological analysis of a search phrase. The algorithm may be, for example, machine-learned about the correspondence between the feature information of a word and a category. The feature information of a word is, for example, a feature vector. When using an algorithm that machine-learns the correspondence between word feature information and categories, the category estimation unit 232 acquires word feature information obtained by morphological analysis of the search phrase, and uses the acquired feature information as the above algorithm. By inputting, the category can be specified. Further, the category estimation unit 232 may specify at least one of a word similarity word obtained by morphological analysis of the search phrase and a co-occurrence word of the word obtained by morphological analysis of the search phrase. The category estimation unit 232 may specify a category by searching the category database using at least one of the specified similar words and co-occurrence words. It should be noted that similar words and co-occurrence words may be specified by using a machine-learned model. In this case, the category estimation unit 232 estimates the category from the words included in the search phrase by machine learning. Further, for example, the category estimation unit 232 may calculate the degree of similarity between the feature vector of a word belonging to a certain category and the feature vector of a word obtained by morphological analysis of a search phrase. In this case, the category estimation unit 232 determines that the word obtained by morphological analysis of the search phrase belongs to a certain category if the calculated similarity is equal to or higher than a predetermined threshold value.

The determined category may be used in the weighting described later. The determined category may be used to narrow down the search range of the search unit 25. When the search unit 25 is used to narrow down the search range, the feature generated by the feature word generation unit 24 is a search target for spots belonging to the category determined by the category determination unit 23 among the spots accumulated in the storage database. Detects the spot to which a word is associated. For example, consider a case where the category discriminating unit 23 determines that the category of the word "landscape" included in the search phrase is "sightseeing spot". In this case, the search unit 25 searches for spots to which the category of "sightseeing spot" is associated among the spots stored in the storage database, using the feature words generated from "landscape". As a result, the information retrieval system 10 can appropriately narrow down the search range and quickly obtain appropriate search results.

(4: Priority in search)
When the feature word generation unit 24 can generate a plurality of feature words, the feature word generation unit 24 may generate only the feature words having a high priority among the plurality of feature words. The priority is, for example, in the information retrieval system 10, the cumulative number of times used for the search in the past, the frequency used for the search in the past predetermined period, and the order calculated based on both the cumulative number of times and the frequency of use, or one of them. It may be at least one of. For example, when the feature word generation unit 24 can generate "scenery" and "restaurant" as feature words, the search frequency of "restaurant" in the last week is higher than the search frequency of "scenery" in the last week. For example, "restaurant" may be used as a characteristic word.

The feature word generation unit 24 can calculate information indicating the number of times or information indicating the frequency of the search phrase received from the information processing terminal 1 within a preset period, and store it as statistical data. Just do it. Further, the stored statistical data is associated with the search phrase received from the information processing terminal 1.

Further, when the feature word generation unit 24 generates a plurality of feature words, the search unit 25 may perform a search using the feature words having a high priority among the plurality of feature words. Further, the search unit 25 may output the search result by the feature word having a higher priority with priority over the search result by the feature word having a lower priority. For example, the search unit 25 may output in order from the search result by the feature word having the highest priority. In addition, the statistical data can also be used for weighting described later.

(5: Construction of storage database)
The information storage device 3 can acquire various information related to the spot to be searched and construct a storage database. The information storage device 3 may generate feature words from articles posted on SNS, for example. The information storage device 3 may generate a feature word from an article posted on a website that introduces a spot or a site that posts word-of-mouth information on a spot, for example. Hereinafter, articles posted on SNS or articles posted on sites that post word-of-mouth information on spots are also referred to as comments. In addition, a website may be abbreviated as a site.

For example, when the information storage device 3 acquires a comment that "the view is the best" posted on the word-of-mouth site about the spot "Observatory X", it morphologically analyzes the acquired comment and "views" and "the best". Get the word. Next, the information storage device 3 calculates a feature vector of a word obtained by morphological analysis, and uses the calculated feature vector to generate a feature word having a feature vector similar to that of each word. Then, the information storage device 3 adds the spot "Observatory X" to the storage database, and records the words obtained by the morphological analysis and the feature words generated from the words in association with each other. If the spot "Observatory X" is already recorded in the storage database, the information storage device 3 adds a word obtained by morphological analysis and a feature word generated from the word to the already recorded spot. Record in association with.

The information storage device 3 may record a co-occurrence word or a cluster described later in association with the feature word instead of or in addition to the feature word. Also, on the site, spots may be evaluated by the number of points or the number of stars. When the spot is evaluated by the number of points, the number of stars, or the like, the information storage device 3 may also record the evaluation of the spot in the storage database. In addition to this, the information storage device 3 may record, for example, the number of access to the site, the number of access to the page of the spot, the number of comments to the spot, the posting date and time of the comment, and the site name in the storage database. good. The site name may be, for example, a service name. These information can be used, for example, for weighting spots, whereby, for example, the information retrieval system 10 can preferentially search for spots with high evaluation.

As described above, the information storage device 3 can expand the storage database based on word-of-mouth information and can search for new spots. The information retrieval system 10 makes it possible to perform a search based on the impressions of people who actually visit the spot. For example, if the information retrieval system 10 records a characteristic word based on the comment that "the view is the best" about the spot "Observatory X", the information retrieval system 10 starts from an ambiguous search phrase such as "the feeling of liberation is the best". It is also possible to detect the spot "Observatory X".

Further, the information retrieval system 10 can detect a spot according to the user's intention even when an ambiguous search phrase is input by constructing a storage database based on information related to the spot. ..

[Search using cluster]
As described in the above embodiment, the feature word generation unit 24 may use one word as a "feature word" or a word group consisting of a plurality of words as a "feature word". Hereinafter, the latter case will be described in more detail with reference to FIG.

When a word group consisting of a plurality of words is used as a feature word, in the storage database, a feature word group consisting of a plurality of feature words having similar feature information indicating the characteristics of the word is associated with a spot to be searched. .. The feature information is, for example, a feature vector. In the following, the feature word group is also referred to as a cluster. When the search phrase is input, the feature word generation unit 24 determines the cluster corresponding to the word based on the feature information of the word included in the input search phrase. Then, the search unit 25 detects the spot associated with the cluster determined by the feature word generation unit 24 from the accumulation database.

According to this configuration, the information retrieval system 10 can detect a valid spot according to the intention of the user performing the search even when a search phrase that is not directly associated with the spot is input. .. For example, in the storage database, when cluster A consisting of similar feature words such as "landscape" and "landscape" and spot A are associated with each other, a search phrase including the word "landscape" is input. do. In this case, in the information retrieval system 10, since the "landscape" is similar to the "landscape" or the "landscape", it is determined that the "landscape" belongs to the cluster A, so that the spot A is detected from the accumulation database. be able to. Whether or not the feature information is similar can be determined by, for example, whether or not the degree of similarity is equal to or higher than a predetermined threshold value.

Further, in the information retrieval system 10, when a word group consisting of a plurality of words is used as a feature word, a category database in which a cluster composed of a plurality of feature words having similar feature information indicating the characteristics of the word and a category are associated with each other is created. You may use it. In this case, the category determination unit 23 detects from the category database the category associated with the cluster determined based on the feature information of the word included in the search phrase. According to this configuration, the information retrieval system 10 can determine an appropriate category according to the intention of the user performing the search even when a search phrase that is not directly associated with the category is input. ..

Hereinafter, a specific example of the spot search according to the flow of FIG. 3 will be described with reference to FIGS. 4 to 6. The category database and the spot database correspond to the storage database. Further, the acquisition of the search phrase and the position information (S201, S202) in FIG. 3 is the same as in FIG. 2, so the description thereof will be omitted.

In S203, the feature word generation unit 24 calculates the feature vector of each word obtained by morphological analysis of the search phrase. FIG. 4 shows an example in which four search phrases of "beautiful scenery" as example 1, "beautiful scenery" as example 2, "beautiful scenery" as example 3, and "fun date" as example 4 are input. ing. For example, when the search phrase of Example 1 is input, in S203, among the words obtained by morphological analysis of "scenery is beautiful", about "scenery" and "beautiful" excluding the particle "ga". The feature vector is calculated. In the illustrated example, an n-dimensional feature vector is calculated. n may be a natural number, for example, n = 300.

Next, the feature word generation unit 24 determines a cluster of each word obtained by morphological analysis of the search phrase (S204). Specifically, the feature word generation unit 24 inputs the feature vector calculated in S203 into a predetermined clustering model, and the cluster corresponding to the feature vector, that is, each word obtained by morphological analysis of the search phrase. Get the output of the cluster. For example, in the example of FIG. 4, it is determined that both "landscape" and "landscape" belong to the cluster whose cluster ID is "15". Further, both "beautiful" and "beautiful" are determined to belong to the cluster whose cluster ID is "8". The clustering model is a model that has been machine-learned so as to output a cluster corresponding to the word by using the feature vector of the word as input data. A cluster corresponding to a word is a cluster containing a feature word having a feature vector similar to that of the word as a component. The method of generating the clustering model will be described later.

Next, the category determination unit 23 determines the category of each word obtained by morphological analysis of the search phrase based on the cluster determined in S204 (S205). For the determination of the category, a category database in which the cluster ID, the feature word, the weight, and the category are associated with each other as shown in FIG. 5 can be used. Specifically, first, the category determination unit 23 extracts the category to which the cluster determined in S204 is associated from the category database. For example, in Example 1 “Scenery is beautiful” in FIG. 4, since the cluster is determined to have the cluster ID “15” and the cluster ID “8” in S204, the category “Famous place "Old site" and the category "Restaurant" are extracted.

Next, the category discriminating unit 23 calculates the similarity between each word obtained by morphological analysis of the search phrase and each characteristic word in the category corresponding to the word, and multiplies the calculated similarity by the weight. In addition, the interword similarity estimation model of fastText may be used for the calculation of the similarity. Then, the calculated values are totaled for each category, and the above-extracted categories are ranked in descending order of the total value. Hereinafter, the total value totaled for each category is also referred to as a score.

For example, in the case of Example 1 "scenery is beautiful" in FIG. 4, the word "scenery" obtained by morphological analysis of the search phrase and the characteristic word "landscape" registered in the category database for the category "famous place / historic site". The similarity of is calculated. The same is true for the word "pretty". Then, the calculated similarity is multiplied by the weight registered in the category database. For example, if the similarity between the word "scenery" and the feature word "landscape" is 0.8, and the similarity between the word "beautiful" and the feature word "beautiful" is 0.7, the category "famous place / historic site" The score of "" is calculated as follows.

About the word "scenery": similarity "0.8" x weight "0.88" = 0.704
About the word "pretty": similarity "0.7" x weight "0.76" = 0.532
Score of category "Famous place / historic site": 0.704 + 0.532 = 1.236
In other words, the score is calculated by multiplying the similarity value and the weight value for each word and adding each of the multiplied values.

When a plurality of feature words having the cluster ID "15" are registered in the category database, the same degree of similarity is calculated for each feature word by multiplying the weights. The same applies to the cluster ID "8". Then, the category determination unit 23 totals the values obtained by multiplication for each category to calculate a score, and ranks the extracted categories in descending order of score.

Next, the search unit 25 searches for spots based on the cluster determined in S204 (S206). For the spot search, a spot database in which the cluster ID, the feature word, the weight, and the spot ID are associated with each other as shown in FIG. 6 can be used. Specifically, first, the search unit 25 extracts a spot to which the cluster determined in S204 is associated from the spot database. For example, in Example 1 “Scenery is beautiful” in FIG. 4, since the cluster is determined to have the cluster ID “15” and the cluster ID “8” in S204, the spot “famous place / historic site” is referred to from the spot database of FIG. , Spot "restaurant" is extracted.

Next, the search unit 25 calculates the similarity between each word obtained by morphological analysis of the search phrase and each feature word of the spot corresponding to the word, and multiplies the calculated similarity by a weight. In addition, the interword similarity estimation model of fastText may be used for the calculation of the similarity. Then, the calculated values are totaled for each spot, and the above-extracted spots are ranked in descending order of the total value. Hereinafter, the total value obtained by summing the calculated values for each spot is also referred to as a score.

For example, in the case of Example 1 "landscape is beautiful" in FIG. 4, the word "scenery" obtained by morphological analysis of the search phrase for the extracted spot "famous place A" and the characteristic word "scenery" registered in the spot database are used. The degree of similarity of "landscape" is calculated. Since the word "Kirei" matches the feature word "Kirei" registered in the spot database, the similarity is 1. Then, the calculated similarity is multiplied by the weight registered in the spot database. For example, if the similarity between the word "landscape" and the feature word "landscape" is 0.9, the score of the spot "famous place A" is calculated as follows.

About the word "scenery": similarity "0.9" x weight "0.53" = 0.477
About the word "pretty": similarity "1.0" x weight "0.46" = 0.46
Score of spot "Famous place A": 0.477 + 0.46 = 0.937
In other words, the score is calculated by multiplying the similarity value and the weight value for each word and adding each of the multiplied values.

When a plurality of feature words having the cluster ID "15" are registered in the spot database, the same degree of similarity is calculated for each feature word by multiplying the weights. The same applies to the cluster ID "8". Then, the values obtained by the multiplication are totaled for each spot, and the above-extracted spots are ranked in descending order of the score which is the total value.

Next, the search unit 25 takes into account the category determination result of S205, and finally ranks the spots detected in S206 (S207). For example, in the category determination of S205, the search unit 25 may rank the spots belonging to the category with the highest score in descending order of the score calculated in S206. It should be noted that which category each spot belongs to is determined in advance.

Then, the search unit 25 ranks all the spots belonging to the category with the highest score, and then ranks all the spots belonging to the category with the next highest score. May be repeated until is determined. In addition, the search unit 25 may end the above-mentioned iterative process at the stage when the predetermined order is determined. As the predetermined ranking, for example, the top 20 ranks may be mentioned.

The ranking method based on the category and spot scores is not limited to the above example. For example, the search unit 25 may calculate the total value of the score of the spot and the score of the category to which the spot belongs for each spot detected in S206, and rank the spots in the order of the total value. In this case, it can be said that the category score is used for weighting the spot score. Further, the above-mentioned statistical data may be added to the ranking of S207. In this case, for example, the ranking of the spots with high evaluation may be high.

Finally, the search communication device 21 notifies the information processing terminal 1 of the search result (S209). As a result, the terminal control device 14 of the information processing terminal 1 causes the display device 13 to display the search result. When displaying the search result on the display device 13, the search communication device 21 may notify the information processing terminal 1 of only a predetermined number of search results having a higher rank determined in S208. As the top predetermined number, for example, the top 10 of the search results can be mentioned.

Further, the search communication device 21 may also notify the information processing terminal 1 of the order determined in S208. In this case, the terminal control device 14 can display the search result on the display device 13 in a display mode according to the order. For example, when the search result is displayed on the map, the higher the ranking of the spots on the map, the larger the marker may be displayed. As a result, the information retrieval system 10 can make the user recognize which of the detected spots is more appropriate.

As described above, the information retrieval system 10 can detect a valid spot from various search phrases by determining a cluster corresponding to the search phrase and searching for a spot using the determined cluster. For example, the same applies regardless of whether the search phrase is Example 1 “Scenery is beautiful” in FIG. 4, Example 2 “Scenery is beautiful” in FIG. 4, or Example 3 “Scenery is beautiful” in FIG. Spots can be detected. Further, when the search phrase is Example 4 "fun date" in FIG. 4 which is completely different from Example 1 to Example 3, a cluster different from Example 1 to Example 3 is determined, and as a different spot accordingly, for example, an amusement park. F is detected.

The cluster can also be used to specify a category, and by specifying a category, spots in the category of that category can be detected. Further, by setting a larger weight for the combination of the spot and the feature word or the combination of the category and the feature word as the combination has a higher relevance, it is possible to preferentially detect the spot with high validity.

(Variation example of category judgment / spot search)
In the category determination of S205, the similarity between the word obtained by morphological analysis of the search phrase and the feature word registered in the category database is calculated, but this process can be omitted. In this case, in S205, the category determination unit 23 may calculate the sum of the weight values of each cluster ID associated with the category, and use the calculated value as the score of the category.

Similarly, in the spot search of S206, the calculation of the similarity can be omitted. In this case, in S206, the search unit 25 may calculate the sum of the weight values of the cluster IDs associated with the spots, and use the calculated values as the score of the category.

[Generate category database and spot database]

(1: Configuration to realize)
The information retrieval system 10 may have a function of generating a category database and a spot database. The components for realizing this function may be provided in, for example, the information retrieval device 2 or the information storage device 3. Further, a device having this function may be separately added to the information retrieval system 10.

Hereinafter, an example in which the information storage device 3 has a function of generating a category database and a spot database will be described with reference to FIG. 7. FIG. 7 is a block diagram showing an example of the main configuration of the information storage device 3. As shown in the figure, the storage control device 32 includes a clustering model generation unit 321, a feature word extraction unit 322, a cluster determination unit 323, a spot database generation unit 324, a category classification unit 325, and a category database generation unit 326. In the following, the database may be abbreviated as DB. The functions of each of these parts can also be realized by a program, for example. In addition, word-of-mouth information written on a predetermined site is used to generate the category database and the spot database. Further, it is assumed that one word of mouth contains at least one sentence. Here, the predetermined site may be a plurality of sites or an SNS. Further, in the following, word-of-mouth information may be simply abbreviated as word-of-mouth.

The clustering model generation unit 321 generates a clustering model for clustering words. Clustering may be performed, for example, based on the similarity of feature vectors, whereby roughly similar language groups can be treated as a single cluster. The "roughly similar language group" can be said to be a rough synonym group. For the calculation of the feature vector of a word, for example, a vectorized model of fastText may be used. The generated clustering model is used to determine the cluster in S204 of FIG. By performing clustering, it is possible to perform a search on a cluster-by-cluster basis, so that the search can be made more efficient and the search can be made faster.

The feature word extraction unit 322 extracts the feature words in the input sentence. For example, co-occurrence word analysis can be used for extracting feature words. Specifically, the feature word extraction unit 322 calculates a TF-IDF value for each word included in the word group obtained by morphological analysis of the input sentence. Then, the feature word extraction unit 322 ranks the words included in the word group according to the calculated TF-IDF value, and extracts the words ranked higher as the feature words. Further, since the TF-IDF value is a numerical value indicating the certainty of the feature word, the feature word extraction unit 322 uses the TF-IDF value of the extracted feature word as the weight value of the feature word.

The cluster determination unit 323 determines a cluster of feature words extracted by the feature word extraction unit 322 using the clustering model generated by the clustering model generation unit 321. The determined cluster is composed of a plurality of feature words having similar feature information.

The spot DB generation unit 324 generates a spot database as shown in FIG. 6 by associating a spot, a feature word, a cluster thereof, and a weight value. The feature word and the weight value are extracted and calculated by the feature word extraction unit 322. The cluster is determined by the cluster determination unit 323.

The category classification unit 325 classifies the input sentence into a plurality of predetermined categories. For classification, a classification model constructed by supervised machine learning may be used. As the classification model, for example, a text classification model of fastText may be used. In machine learning, sentences with known categories are used as teacher data. The teacher data may be manually associated with categories for each of the word-of-mouth written on a predetermined site, for example.

The category DB generation unit 326 generates a category database as shown in FIG. 5 by associating a category, a feature word, a cluster, and a weight value. The feature word and the weight value are extracted and calculated by the feature word extraction unit 322. The cluster is determined by the cluster determination unit 323.

(2: Process flow)
Hereinafter, an example of the flow of processing for generating the spot database will be described with reference to FIG. When the clustering model generation unit 321 starts the process of generating the spot database, the clustering model generation unit 321 generates a clustering model (S301). For example, the clustering model generation unit 321 calculates a feature vector for all words obtained by morphological analysis of all the word-of-mouth written in a predetermined site. The clustering model generation unit 321 can generate a clustering model based on the calculated feature vector. In this case, the generated clustering model is a model that classifies the input word into clusters in which the feature vector and the feature vector of the input word are similar. For example, fastText can also be used to generate the model.

Next, the feature word extraction unit 322 extracts feature words and weights from the word-of-mouth of each spot (S302). Extraction of feature words and weights can be performed using, for example, a TF-IDF value. The correspondence between each word-of-mouth and the spot is specified in advance according to which spot the word-of-mouth is written for. For example, if the word-of-mouth is written on the page introducing the famous place A on a predetermined site, the feature word extraction unit 322 determines that the word-of-mouth is the word-of-mouth of the famous place A by storing it in association with the famous place A. Can be identified.

Next, the cluster determination unit 323 determines the cluster of the feature words extracted in S302 using the clustering model generated in S301 (S303). By the processing of S302 and the processing of S303, the feature word, the weight, and the cluster are determined for each of the word-of-mouth written for each spot.

Next, the spot DB generation unit 324 stores the determined feature words, weights, and clusters in the spot database in association with the spots (S304). As a result, a record in which the feature word, the cluster, and the weight are associated with each spot is added to the spot database. If the spot database has not been generated, the spot database is generated in S304.

Subsequently, an example of the process of generating the category database will be described with reference to the flowchart shown in FIG. When the category classification unit 325 starts the process of generating the category database, each word-of-mouth written in the predetermined site is classified into categories (S401).

Next, the feature word extraction unit 322 extracts feature words and weights from the word-of-mouth of each category (S402). Extraction of feature words and weights can be performed using, for example, a TF-IDF value. In S402, the feature words and weights extracted in S302 may be diverted.

Next, the cluster determination unit 323 determines the cluster of the feature words extracted in S402 using the clustering model generated in S301 (S403). For each of the word-of-mouth of each category, the feature word, the weight, and the cluster are determined by the processing of S302 and the processing of S403.

Finally, the category DB generation unit 326 stores the determined feature words, weights, and clusters in the category database in association with the corresponding categories (S404). As a result, a record in which the feature word, the cluster, and the weight are associated with each category is added to the category database. If the category database has not been generated, the category database is generated in S404.

(3: Summary)
As described above, the clustering model generation unit 321 calculates a feature vector for each word obtained by morphological analysis of word-of-mouth, and generates a clustering model based on the calculated feature vector. According to the generated clustering model, search phrases containing expressions similar to word-of-mouth can be accurately clustered. The clustering model can also be generated from word-of-mouth only.

Then, by using the constructed clustering model and word-of-mouth, a spot database and a category database can be generated. Spot databases and category databases can also be generated from word-of-mouth only. It is also possible to expand these databases by acquiring the latest reviews from time to time. And since the word-of-mouth reflects the latest terms used when evaluating or expressing spots and categories, by using each generated database, it is possible to support searches using the latest terms. Can be done.

Further, the cluster determination unit 323 determines the cluster of the feature word using the clustering model, and the spot DB generation unit 324 stores the cluster determined by the cluster determination unit 323 in association with the spot. As a result, the search unit 25 can detect a valid spot by using the cluster corresponding to the word even when a search phrase consisting of words that are not directly associated with the spot is input. ..

Similarly, the category DB generation unit 326 stores the cluster determined by the cluster determination unit 323 and the category classified by the category classification unit 325 in association with each other. As a result, the category determination unit 23 can determine an appropriate category by using the cluster corresponding to the word even when a search phrase consisting of words that are not directly associated with the category is input. can.

Further, the feature word extraction unit 322 extracts the feature word and calculates the weight thereof, and the spot DB generation unit 324 stores the spot, the cluster, the feature word, and the weight in association with each other. As a result, each feature word included in one cluster can specify the magnitude of the relationship with the spot by the weight value. Therefore, the search unit 25 can rank the search results according to the magnitude of the relationship between the feature word corresponding to the search phrase and the spot.

Similarly, the category DB generation unit 326 stores categories, clusters, feature words, and weights in association with each other. As a result, for each feature word associated with one category, the magnitude of the relationship with the category can be specified by the weight value. Therefore, the category determination unit 23 can rank the determination result of the category according to the magnitude of the relationship between the feature word corresponding to the search phrase and the category.

(About re-learning)
When a model generated by supervised machine learning is used in the search, teacher data corresponding to the search result of the search may be generated, and the generated model may be updated by performing machine learning again with the teacher data. For example, it is assumed that a certain search phrase is input, a category determination (S204) and a spot search (S206) are performed, and one of the spots notified to the information processing terminal 1 is selected by the user. In this case, it is considered that the selected spot was a spot in line with the user's intention. Therefore, the search phrase or the word constituting the search phrase, its feature vector, and the spot or its category can be associated with each other to obtain teacher data. For example, by associating the search phrase with the category of the selected spot, it may be used as teacher data for updating the classification model used by the category classification unit. By updating the classification model by re-learning using the teacher data, the classification accuracy of S401 can be improved.

[Variation example of generation of category database and spot database]

(1: Configuration to realize)
Hereinafter, a modified example in which the information storage device 3 is provided with a category database and a spot database generation function different from those in FIG. 7 will be described with reference to FIG. The information storage device 3 includes a timekeeping device 34 in addition to the storage storage device 31, the storage control device 32, and the storage communication device 33. The function of each part can be realized by a program, for example. In other words, the information storage device 3 shown in FIG. 7 and the information storage device 3 shown in FIG. 10 are configured in the same manner except that the timekeeping device 34 is connected to the storage control device 32. Hereinafter, the description overlapping with the description of FIG. 7 will be omitted. Further, word-of-mouth information is used as posted article information posted on a predetermined site of the external information processing apparatus 4 to generate the category database and the spot database. The information storage device 3 and the external information processing device 4 are connected by wireless communication, for example, via a public communication network 50. In FIG. 10, a double-headed arrow illustrates the flow of information in which communication is performed via the public communication network 50.

In the information storage device 3 shown in FIG. 10, the storage control device 32 is electrically connected to the timekeeping device 34. The storage control device 32 is configured to be able to acquire time information from the time measuring device 34. The spot DB generation unit 324 generates a spot database by associating time information with spots, feature words, clusters, and weight values. The category DB generation unit 326 generates a category database by associating time information in addition to categories, feature words, clusters, and weight values. The time information is, for example, information on the time measured by the time measuring device 34. Similar to the timekeeping unit 26 of the information retrieval device 2, the timekeeping device 34 is configured to be able to time a year, a month, a day, and an hour by executing a timekeeping program on a computer.

(2: Process flow)
FIG. 11 shows an example of a process of updating the spot database and the category database when the information storage device 3 acquires word-of-mouth information from the external information processing device 4. The information storage device 3 acquires word-of-mouth information from the external information processing device 4 by the storage communication device 33 at predetermined period intervals (S111). More specifically, the information storage device 3 collects word-of-mouth information via WebAPI of SNS.

Further, the information storage device 3 analyzes new word-of-mouth information among the acquired word-of-mouth information. In order to analyze the new word-of-mouth, the information storage device 3 discriminates whether or not the new word-of-mouth is written within a predetermined period. The predetermined period may be, for example, a time retroactive by a predetermined time from the current time measured by the time measuring device 34. Next, the information storage device 3 analyzes a new word-of-mouth by machine learning (S112). The analysis of the new word-of-mouth can be performed in the same manner as the processing of FIGS. 8 and 9.

The information storage device 3 counts the number of times a new spot is detected within a predetermined period as a result of analyzing the new word-of-mouth. The information storage device 3 can determine whether or not the spot analyzed from the word-of-mouth information is a new spot by comparing with the spot database 311 in which the spot is registered in advance. In order to count the number of times a new spot is detected, the information storage device 3 stores the number of times a new spot is detected in the temporary database 312 shown in FIG. In the information storage device 3, the temporary database 312 may be stored in, for example, the storage storage device 31. In the information storage device 3, if the number of detections of new spots is greater than the predetermined number of thresholds or the number of thresholds, the spot database generation unit 324 adds the new spots to the spot database 311 (S113). The spot database 311 may be stored in, for example, the storage storage device 31.

In the information storage device 3, if the number of detections of new spots is less than the predetermined threshold number in a predetermined unit time, the spot database generation unit 324 deletes the new spots from the temporary database 312 (S114). The predetermined unit time may be the same as or different from the predetermined period.

Next, the information storage device 3 analyzes word-of-mouth information for existing spots. The information storage device 3 discriminates whether or not the spot analyzed from the word-of-mouth information is an existing spot by comparing with the spot database 311 in which the spot is registered in advance. The information storage device 3 uses machine learning to analyze word-of-mouth to existing spots (S115). The analysis of word-of-mouth to the existing spot can be performed in the same manner as the processing of FIGS. 8 and 9.

In the information storage device 3, the step of analyzing the comment of the new word-of-mouth and the step of analyzing the word-of-mouth to the existing spot do not have to be in the order of the new analysis step of S112 and the existing analysis step of S115, and the existing analysis. A new analysis step may be performed after the step. In the information storage device 3, a new analysis step for analyzing a new word-of-mouth and an existing analysis step for analyzing a word-of-mouth to an existing spot may be performed in parallel.

(3: Summary)

[Other variants]
Hereinafter, a modification of the information processing terminal 1 will be described in detail. In the information processing terminal 1, when the user inputs some character in the search box for inputting the character string to be searched, the information search device 2 searches for the search phrase related to the input character, and the information search is performed at the bottom of the search box. It is configured to display the search result candidates searched by the device 2.

The information processing terminal 1 is configured to be able to bookmark a place where the user wants to go or a place where the user has gone. In the information processing terminal 1, the user checks whether or not the user has performed the check box, so that the user can check whether the check box has been performed by the user. The place where the user wants to go or the place where the user bookmarks is the basis of the recommendation. In the information processing terminal 1, the user may have visited a predetermined place by using the location information.

In the information processing terminal 1, a standard word that the user will often use and a hot word that the user will recommend are displayed on the display device 13. The information processing terminal 1 is configured to be able to perform a search from standard words and hot words in addition to the search phrase entered by the user in the search box of the information processing terminal 1.

The information processing terminal 1 is configured to reflect the new comment if there is a new comment in the existing spot. The information processing terminal 1 can access the information storage device 3 via the information retrieval device 2, detect a spot within a predetermined period of the information storage device 3, and display the new spot on the display device 13. As shown in FIG. 12, the information processing terminal 1 causes the display device 13 to display a count value obtained by increasing the count for each predetermined reference value at the lower right of the icon 151 on the map 150. The count value is, for example, a numerical value that increases the count for each predetermined reference value when, for example, a predetermined number or more of users leave favorable comments on the word-of-mouth. The information processing terminal 1 may display an existing spot or a new spot by changing the color of the icon 151. The information processing terminal 1 may show, for example, a heart mark 153 indicating a spot desired to go to the upper left of the icon 151. The information processing terminal 1 may show, for example, a star mark 154 indicating a recommendation in the upper right corner of the icon 151.

The information processing terminal 1 is not limited to a configuration in which map information is stored in the terminal storage device 15 in advance. The information processing terminal 1 may be configured such that map information is stored in the terminal storage device 15 by communication from the information retrieval device 2. As the map information, for example, topographic maps, topographic maps, geological maps, land use maps, residential maps, route maps, road maps, guide maps, aerial photographs, satellite photographs, or old maps can be used. The map information may be, for example, raster data or vector data.

The information processing terminal 1 may have a navigation function. When the information processing terminal 1 is provided with a navigation function, the information processing terminal 1 may be configured to display recommendations on a map when it approaches a checkpoint on the route. The checkpoint may be, for example, a POI that the user wants to recommend, a treasure reserve spot in a treasure hunt game, or a charging station for an electric vehicle. When the information processing terminal 1 obtains information from a plurality of SNSs, the information processing terminal 1 may register another ID associated with the login IDs of the plurality of SNSs.

Hereinafter, a modification of the information retrieval device 2 will be described in detail. The information retrieval device 2 searches for a search phrase by presuming that words that frequently appear in word-of-mouth communication collected from SNS or the Web during a predetermined period are most related to each of the search history, hot words, and standard words. The information retrieval device 2 may be configured to search a combination of a search history, a hot word, and a standard word. A hot word is a word that has been statistically processed per unit time. Hot words are words that frequently appear in word-of-mouth per unit time. The hot word may be a word of a spot within a predetermined range from a search point in addition to a word statistically processed per unit time. This allows hotwords to include events such as local festivals. In other words, a hot word can be said to be a recommended word for users. A standard word is a word that is presumed to be used frequently by the user.

Hereinafter, spots with high topicality are also referred to as hot spots. When searching for a hotspot, the information retrieval device 2 may be configured to search from information within a predetermined period from the current time in the storage database stored in the storage storage device 31.

The information retrieval device 2 may perform a search by operating the information processing terminal 1 by the user, for example, based on hotspot information and usage history information in addition to a search phrase. Examples of the hotspot information include new spots newly registered in the spot database 311 in the storage storage device 31 of the information storage device 3. Examples of the usage history information include search history information searched by the user in the past and registration information registered by the user in the past. In other words, the usage history information can be said to be operation history information in which the user operates the information processing terminal 1.

Since the information retrieval device 2 searches from the information storage device 3 using the user's usage history information, the searched search target is optimized for each user. In other words, the information retrieval device 2 allows the user to use the usage history even if a large amount of user attribute information indicating the user's attribute is not stored in the storage storage device 31 of the information storage device 3 in advance as a parameter. It is structured so that it can provide recommended words for it. The information retrieval device 2 may acquire the position information acquired from the sensor that acquires the position information from the base station of public communication in addition to the GPS that the information processing terminal 1 is a satellite positioning system. The information retrieval device 2 is a terminal communication device 12 of the information processing terminal 1 so that when a new spot is registered in the storage storage device 31 of the information storage device 3, the hot spot can be displayed on the display device 13 of the information processing terminal 1. It may be configured to be able to push notifications to.

Hereinafter, a modification of the information storage device 3 will be described in detail. The information storage device 3 may acquire word-of-mouth information from a plurality of external information processing devices 4. When analyzing word-of-mouth information, the information storage device 3 is preferably configured so that only Japanese can be extracted in advance from word-of-mouth information, or spaces or symbols can be deleted. Word-of-mouth is often not in accurate Japanese. Word-of-mouth may also include symbols. The word-of-mouth information may not be able to perform the desired morphological analysis if the word-of-mouth is not in accurate Japanese or if it contains symbols. When analyzing word-of-mouth information, the information storage device 3 can perform a more accurate search by extracting only Japanese from the word-of-mouth information in advance or deleting spaces or symbols.

In the information storage device 3, for example, when a predetermined number or more of users leave favorable comments on word-of-mouth, the count number obtained by increasing the count for each predetermined reference value becomes a number larger than the predetermined count number. , Is configured to be registered in the spot database 311 as a new spot. The information storage device 3 determines whether the registered new spot is, for example, a cafe, a restaurant, or a landscape. The information storage device 3 registers the new spot together with the determined category in the category database. In other words, the information storage device 3 stores a hot word or a standard word in the storage storage device 31.

From the above, the information retrieval system 10 uses the usage history information associated with the individual user when the user inputs "ramen" in the search box even if the user does not know the name of the "tonkotsu ramen shop" that the user has previously visited. You can search for "the name of the tonkotsu ramen shop".

Further, even if the information processing terminal 1 is exchanged and the user's search history disappears from the information processing terminal 1, the information retrieval system 10 exchanges information from the usage history information registered in the information retrieval device 2 or the information storage device 3. A similar search can be performed on the information processing terminal 1.

The information storage device 3 of the information retrieval system 10 is based on a list of places the user wants to go or a list of places where the user has visited, and a search history of seeing the search results by stepping into a detailed screen. Create a search type for each user who is interested in. The information retrieval device 2 can discriminate between the user's search type and other users of similar types by machine learning. The information processing terminal 1 can be performed on a user who has searched for recommendations of other spots made by another user. That is, the information retrieval system 10 has, for example, a list of places the user wants to go or a list of places where the user has visited, and a search history in which the search results are stepped into a detailed screen when the user frequently uses the information. Based on the above, it is configured so that the recommended location can be recommended to the user.

The information retrieval system 10 determines by machine learning whether it is a valid comment that recommends something from the word-of-mouth added in the SNS, for example, "I went to lunch today, XX was delicious". In the case of a recommended valid comment, the information processing terminal 1 outputs the recommended location on the map displayed on the display device 13. In other words, the information storage device 3 selects new positive comments recommended to everyone from SNS reviews by machine learning, and displays spots related to the comments on the map.

10 Information retrieval system 11 Input device 13 Display device 22 Location information acquisition unit 24 Feature word generation unit 25 Search unit

Claims (4)

An input device where a search phrase is entered, and
A feature word generation unit that generates feature words related to words included in the search phrase from the search phrase, and
A search unit that searches for the search target from a database that stores the feature word and the search target corresponding to the feature word, and a search unit.
A display device that displays the search target and
It is equipped with an information storage device for registering the search target in the database.
The information storage device is
Acquire new word-of-mouth information written within the period from the current time to the time retroactive to the specified time from the external information processing device.
Whether or not the word-of-mouth information includes a spot indicating a specific point is analyzed by machine learning.
As a result of analysis, if a new spot that is not registered in the database is detected, the new spot is temporarily saved and saved.
After the temporary storage, the number of detections of the new spot is counted for a predetermined period.
When the number of detections is greater than a predetermined threshold value, the new spot is registered in the database as the search target.
An information retrieval system characterized by that. It also has a location information acquisition unit that acquires location information indicating the location on the map that is the starting point of the search.
The search target has point information indicating a specific point, and has.
The information search system according to claim 1, wherein the search unit searches for the search target corresponding to the feature word within a predetermined range from the position based on the feature word and the position information. In the database , a feature word group composed of a plurality of the feature words having similar feature information indicating the features of the word is associated with the search target.
The feature word generation unit determines the feature word group corresponding to the word based on the feature information, and determines the feature word group.
The information retrieval system according to claim 1 or 2, wherein the search unit searches the database for the search target associated with the feature word group determined by the feature word generation unit. The display device displays claims 1 to 3 in which the color of the first icon indicating the search target registered in the database and the color of the second icon indicating the new spot are displayed differently. The information retrieval system described in any one of the above.

Images