JP6076285B2 - Translation apparatus, translation method, and translation program - Google Patents

Description

  The present invention relates to an apparatus, a method, and a program for translating enumeration data of a large number of noun phrases such as so-called map notes that are character information described on a map.

  Conventionally, various inventions relating to machine translation devices have been made. For example, Patent Document 1 described later describes that even when a proper noun is translated, it can be accurately translated by properly preparing a proper noun dictionary. In addition, the same document also includes a captured word dictionary that stores captured words for each proper noun registered in the proper noun dictionary, so that the proper result is included in the translation result. It is described. This is advantageous in that the translation result can be made easier to understand.

JP 2004-220416 A

  In the case of a conventional machine translation device, various dictionaries are required for translation. The invention described in Patent Document 1 described above also requires a captured word dictionary in addition to a translation dictionary such as proper nouns. Since it takes a certain amount of time to search the dictionary for translation, it takes time to translate a list of noun phrases such as a large number of proper nouns. For example, consider the case of translating map notes as an example of translating a large number of noun phrases.

  There are various maps such as city maps, road maps, regional maps, regional maps, and national maps, and there are a plurality of maps of different scales even with the same type of map. Thus, when all types of maps of different types and scales are targeted, there are tens of millions of map notes. And even if map annotations consisting only of numbers, alphabets, and icons are excluded, those that do not need to be translated are excluded, and even if multiple overlapping map notes are consolidated into one, millions of map notes are subject to translation. Exists.

  It takes several weeks to machine-translate these millions of map notes into multiple languages using a conventional dictionary reference method. In addition, map notes always change when features on the map are lost or newly created, so it is necessary to conduct a field survey at a predetermined timing to update and re-translate.

  As described above, since map annotation needs to be repeatedly translated, there is a conventional request to translate as fast as possible. This also occurs when translating a large number of noun phrases such as place names, names, names, addresses, etc., not only in map notes but also in address lists, phone book lists, customer lists, etc. It is a problem.

  In view of the above, an object of the present invention is to make it possible to translate a large amount of noun phrase enumeration data much faster and more accurately than conventional machine translation devices.

In order to solve the above problem, a translation device according to claim 1 is provided:
A phonetic dictionary that associates strings with their phonetic kana,
First program creating means for converting a content of the reading-reading kana dictionary into program logic and creating a reading-kana adding program for adding a reading kana to an input character string;
A translation dictionary associating a reading kana with a character string of a predetermined language corresponding to the reading kana,
Second program creating means for converting the contents of the translation dictionary into program logic and creating a translation program for translating the input reading pseudonym into a predetermined language;
A reading pseudonym giving unit that executes the reading pseudonym giving program created by the first program creation unit and gives a reading pseudonym to a character string to be translated;
A translation unit configured to execute the translation program created by the second program creation unit and translate the reading pseudonym given by the reading pseudonym granting unit into a target language.

  According to the translation device of the first aspect of the present invention, the reading kana dictionary is converted into the program logic by the first program creating means, and the reading kana is given to the inputted character string. It becomes a reading pseudonym grant program. Similarly, the translation dictionary is converted into a program logic by the second program creation means, and becomes a translation program for translating reading kana into a predetermined language.

  In the translation apparatus, the reading-kana conversion unit executes the reading-kana adding program created by the first program creating unit, and gives a reading-kana to the character string to be translated. Since the contents of the reading kana dictionary are directly used as the program logic, the reading kana giving program does not need to refer to an external reading kana dictionary at all.

  In the translation apparatus, the translation means executes the translation program created by the second program creation means, and translates the reading kana given by the reading kana giving means into the target language. In the translation program, the contents of the translation dictionary are directly used as program logic, so there is no need to refer to an external translation dictionary.

  In this way, the translation device does not refer to an external dictionary at all when adding a reading kana to a character string to be translated or when translating a given reading kana into a target language. Therefore, translation can be performed at extremely high speed. Therefore, even if there are many character strings to be translated, translation can be performed at a very high speed.

  According to the present invention, for example, a large number of noun phrases such as map notes can be translated significantly faster and more accurately than conventional machine translation devices.

It is a block diagram for demonstrating the example of a structure of the translation apparatus of embodiment, and the outline | summary of operation | movement. It is a figure which shows an example of the proper noun reading kana dictionary in the reading kana dictionary group 111. FIG. FIG. 3 is a diagram showing an example of a reading kana giving program formed by converting the proper noun reading kana dictionary shown in FIG. 2 into program logic. It is a figure for demonstrating the example in the case of translating input data "23,138, Kominatotsu". It is a figure for demonstrating the outline | summary of the translation process in the conventional translation apparatus. It is a flowchart for demonstrating the prior preparation process for the translation process performed with the translation apparatus of embodiment. It is a flowchart for demonstrating the translation process performed with the translation apparatus of embodiment.

  Hereinafter, embodiments of the apparatus, method, and program of the present invention will be described with reference to the drawings. The present invention can be applied to translation of enumerated data of a large number of noun phrases such as place names, names, names, and addresses described in map notes, telephone directory lists, customer lists, and the like. The term “noun phrase” means a phrase indicating a specific object such as an object, a substance, a person, or a place. In this specification, as a specific example of “a list of noun phrases” to be translated, for example, a place name, a name, a name, an address described in a map note, a phone book list, a customer list, etc. Yes.

  In the following, a specific description will be given by taking as an example the case of translating a map note. Map annotations exist in large quantities as described above, and are frequently updated. Therefore, map annotations must be re-translated each time they are updated. In recent years, the number of tourists and business people visiting Japan from abroad has increased, and the latest maps for foreign languages, which are translated from the map annotations of the latest maps into the specified foreign languages, can be quickly displayed. What you want to provide. In addition, foreign languages are required to support a plurality of languages such as Chinese (simplified), Chinese (traditional), English, and Korean.

  Under such a background, the apparatus, method, and program according to the embodiments described below are capable of converting a large amount of map annotations significantly faster and more appropriately than a conventional machine translation apparatus, and It has been translated into multiple languages.

[Configuration example of big data translation device]
First, a configuration example and an outline of operation of a big data translation apparatus (hereinafter simply referred to as a translation apparatus) of this embodiment will be described. FIG. 1 is a block diagram for explaining a configuration example and an outline of operation of the translation apparatus according to this embodiment. In the translation apparatus shown in FIG. 1, the control unit 101 implements a function for controlling each unit of the translation apparatus, the storage device 102 implements an information storage holding function, and the operation unit 103 implements a user interface function.

  The reading kana dictionary group 111 includes a plurality of reading kana dictionaries such as a proper noun reading kana dictionary, a general noun reading kana dictionary, a prefix reading kana dictionary, a suffix reading kana dictionary, a katakana reading kana dictionary, and a Japanese map. Provides reading kana for note data. Although not shown, the prefix reading kana dictionary includes a proper noun prefix reading kana dictionary, a general noun prefix reading kana dictionary, and a katakana prefix reading kana dictionary. Similarly, the suffix reading kana dictionary includes a proper noun suffix reading kana dictionary, a general noun suffix reading kana dictionary, and a katakana suffix reading kana dictionary.

  Each of the proper noun reading kana dictionary, the general noun reading kana dictionary, and the katakana reading kana dictionary is capable of searching for kanji reading kana by partial matching. Each prefix reading kana dictionary provides the reading kana of the prefix, so that it is possible to search for kanji reading kana by forward matching. Each suffix reading kana dictionary provides the reading kana of the suffix, so that the kanji reading kana can be searched by backward matching.

  The reading kana giving program creation unit (first program creating means) 112 converts the contents of each reading kana dictionary in the reading kana dictionary group 111 into program logic, and gives the reading kana giving the reading kana to the map note data. Create a program. The reading pseudonym assignment program created by the reading pseudonym assignment program creation unit 112 is stored in the reading pseudonym assignment program storage unit 113. The reading pseudonym assignment program storage unit 113 is formed in a predetermined storage area of the storage device 102, for example.

  FIG. 2 is a diagram illustrating an example of the proper noun reading kana dictionary in the reading kana dictionary group 111. FIG. 3 is a diagram showing an example of a reading pseudonym assignment program created by the reading pseudonym assignment program creation unit 112 from the example of the proper noun reading kana dictionary shown in FIG. .

  As shown in FIG. 2, the reading kana dictionary includes an area code, an attribute number, a proper noun, and its reading kana. The area code is information that uniquely identifies a predetermined area. As the predetermined area specified by the area code, for example, there is a prefecture. Of course, it is possible to make the area more detailed than the prefecture. The attribute number indicates what kind of attribute the proper noun used as the map annotation is, and for example, it is classified into 100 or more attributes (layers) such as place names, educational institutions, public institutions, mountains, rivers, etc. Yes.

  Moreover, in FIG. 2, a proper noun is a proper noun used as a map note, and a reading kana is a reading kana of a corresponding proper noun. An example of the proper noun reading kana dictionary shown in FIG. 2 shows a part of a place name (attribute number = 138) in Aichi Prefecture (area code = 23). In addition, although the example of the proper noun reading kana dictionary was shown here, the other general noun reading kana dictionary, the prefix reading kana dictionary, the suffix reading kana dictionary, and the katakana reading kana dictionary are basically shown in FIG. It has the same configuration as the proper noun reading kana dictionary.

  When the proper noun reading kana dictionary shown in FIG. 2 is converted into program logic by the reading kana giving program creation unit 112, the reading kana giving program shown in FIG. 3 is obtained. The reading pseudonym assignment program shown in FIG. 3 is written in Perl as a programming language. The reading kana giving program shown in FIG. 3 outputs a map note and a reading kana when the area code and the attribute number match and the map note (a place name which is a proper noun in this example) matches.

  If a specific example is shown, the reading kana giving program creation unit 112 creates the reading kana giving program shown in FIG. 3 from the proper noun reading kana dictionary shown in FIG. In this embodiment, the kana reading of the map note is specified by the longest match method. For this reason, the reading-kana adding program creating unit 112 creates a program in which the contents of the reading-kana dictionary are rearranged in the order of long noun phrases (proper nouns in the examples shown in FIGS. 2 and 3). As can be seen from the reading-a-kana giving program shown in FIG. 3, the contents of the reading-a-kana dictionary are directly program logic, so that the program does not need to refer to an external dictionary at all.

  Actually, since the reading-kana dictionary group 111 includes a plurality of reading-kana dictionaries as described above, the reading-kana adding program created by the reading-kana adding program creating unit 112 corresponds to each reading kana dictionary. It has a program logic part. More specifically, the reading pseudonym assignment program includes an initial processing unit, a user dictionary providing unit, a proper noun suffix dictionary (backward match) logic unit, a proper noun prefix dictionary (forward match) logic unit, a proper noun dictionary (part) Match) logic part, general noun suffix dictionary (backward match) logic part, general noun prefix dictionary (forward match) logic part, general noun dictionary (partial match) logic part, Katakana general noun suffix dictionary (backward match) ) Logic unit, Katakana general noun prefix dictionary (forward match) logic unit, Katakana general noun dictionary (partial match) logic unit, exception processing unit, and termination processing unit.

  At the time of creation (generation) of each of the logic units described above, the reading-kana giving program creating unit 112 (1) dictionary data (area code, attribute number, noun phrase, (Reading Kana) is extracted. Then, (2) the reading pseudonym assignment program creating unit 112 rearranges the extracted dictionary data in the order of the noun phrase having the largest number of characters. This is because it is possible to extract a reading kana by the longest match. The processes (1) and (2) are repeated until there is no dictionary data with the same area code and attribute number from the dictionary to be processed, and when there is no more, the process moves to processing of dictionary data having the next area code and attribute number.

  In this way, when processing is completed for all dictionary data for processing, a logic unit corresponding to the reading-kana dictionary for the processing can be created (generated). Then, by creating a program logic for each reading-kana dictionary in the reading-kana dictionary group 111, each reading-kana dictionary used for forward matching, backward matching, and partial matching is extracted with the longest matching method. Can be converted to program logic.

  In this embodiment, the language-specific translation dictionary group 121 includes language-specific translation dictionaries for translating the reading pseudonym of Japanese map annotation data into a target foreign language. In this embodiment, a four-language translation dictionary is provided to support four languages: Chinese (simplified), Chinese (traditional), English, and Korean. Furthermore, the translation dictionaries for each language are roughly divided into a dictionary for general nouns and a dictionary for katakana. The dictionary for general nouns consists of a general noun suffix dictionary, a general noun prefix dictionary, and a general noun dictionary. The dictionary for Katakana is Katakana proper noun suffix dictionary, Katakana proper noun prefix dictionary, Katakana proper noun dictionary, Katakana general noun suffix dictionary, Katakana general noun prefix dictionary, Katakana general noun dictionary Consists of.

  Each of the general noun dictionary, the katakana proper noun dictionary, and the katakana general noun dictionary is capable of searching translation data (translation word / phrase) corresponding to a reading kana by partial matching. Each prefix dictionary provides translation data corresponding to the prefix, so that the prefix translation data can be searched by forward matching. Since each suffix dictionary provides suffix translation data, the suffix translation data can be searched by backward matching.

  The translation dictionary included in the language-specific translation dictionary group 121 also includes an area code and an attribute number, as in the case of the reading kana dictionary shown in FIG. In the case of a translation dictionary, the “proper noun” in FIG. 2 is “Yomikana” or the like, and the “Yomikana” in FIG. 2 is “a character string (translation data) in a predetermined language”.

  The language-specific translation program creation unit (second program creation means) 122 converts the contents of each translation dictionary in the language-specific translation dictionary group 121 into program logic, and converts the reading kana of map annotation data into a predetermined language. Create a language-specific translation program. The language-specific translation program created by the language-specific translation program creation unit 122 is stored in the language-specific translation program storage unit 123. The language-specific translation program storage unit 123 is formed in a predetermined storage area of the storage device 102, for example.

  The program logic of the language-specific translation program created by the language-specific translation program creation unit 122 is the same as that of the reading-kana conversion program shown in FIG. In other words, the language-specific translation program created by the language-specific translation program creation unit 122 has the same area code and attribute number, and if the reading kana and the like (in this example, the reading kana of a map note) match, A character string (translation data) in a predetermined language corresponding to a kana or the like is output.

  Actually, since the language-specific translation dictionary group 121 includes a plurality of translation dictionaries as described above, the language-specific translation program created by the language-specific translation program creation unit 122 is a program corresponding to each translation dictionary. It has a logic part. More specifically, the language-specific translation program includes an initial processing unit, a user dictionary providing unit, a general noun suffix dictionary (backward match) logic unit, a general noun prefix dictionary (forward match) logic unit, a general noun dictionary (part) Match) Logic part, Katakana proper noun suffix dictionary (backward match) logic part, Katakana proper noun prefix dictionary (forward match) logic part, Katakana proper noun dictionary (partial match) logic part, Katakana general noun It consists of a suffix dictionary (backward match) logic unit, a katakana general noun prefix dictionary (forward match) logic unit, a katakana general noun dictionary (partial match) logic unit, an exception processing unit, and an end processing unit.

  When creating (generating) each logic unit described above, the language-specific translation program creating unit 122 (A) dictionary data of the same area code and attribute number (area code, attribute number, noun phrase, translation) from the translation dictionary to be processed Data). Then, (B) the language-specific translation program creation unit 122 rearranges the extracted dictionary data in descending order of the number of characters in the noun phrase. This is because translation data can be extracted by the longest match. The processes (A) and (B) are repeated until there is no dictionary data with the same area code and attribute number from the dictionary to be processed. When there is no more dictionary data, the process moves to processing of dictionary data having the next area code and attribute number.

  In this way, when processing is completed for all dictionary data to be processed, a logic unit corresponding to the translation dictionary to be processed can be created (generated). Then, by creating a program logic for each translation dictionary in the language-specific translation dictionary group 121, each translation dictionary used for forward matching, backward matching, and partial matching is read by the longest matching method to extract a kana. Can be converted to logic.

  A translation target data file (described as translation target data F in FIG. 1) 131 holds a large number of map note data to be translated. Specifically, the translation target data file 131 holds a large number of translation target data composed of “area code, attribute number, map annotation data”. The translation target data file 131 stores, for example, translation target data provided from an external device through an external interface (not shown).

  The reading pseudonym assignment program execution unit 132 reads and executes the reading pseudonym assignment program created by the reading pseudonym assignment program creation unit 112 and stored in the reading pseudonym assignment program storage unit 113. As a result, a reading pseudonym is assigned to the map annotation data of the translation target data stored in the translation target data file 131 by the executed reading pseudonym assignment program.

  In this way, the reading kana data formed by adding the reading kana to the translation target data is recorded in the reading kana data file (described as reading kana data F in FIG. 1) 133. The reading kana data consists of “area code, attribute number, map note data, corresponding reading kana”. The reading kana data file 133 is created on a large-capacity recording medium such as a hard disk.

  The language-specific translation program execution unit 134 reads and executes the language-specific translation program created by the language-specific translation program creation unit 122 and stored in the language-specific translation program storage unit 123. As a result, the reading kana of each reading kana data stored in the reading kana data file 133 is translated into a predetermined language by the executed language-specific translation program, and the translation data is added to the reading kana data. Language-specific translation data is formed.

  The language-specific translation data created by the language-specific translation program executed by the language-specific translation program execution unit 134 is recorded in the language-specific translation data file 135. The language-specific translation data includes “area code, attribute information, map note data, corresponding reading pseudonym, character string (translation data) in a predetermined language”. The language-specific translation data file 135 is created on a large-capacity recording medium such as a hard disk.

  Thus, the translation apparatus of this embodiment converts the reading kana dictionary into program logic, creates a reading kana giving program, and uses this reading kana giving program to map note data to be translated. Give a reading. In addition, the translation device of this embodiment converts a language-specific translation dictionary into program logic, creates a language-specific translation program, and uses this language-specific translation program to give to map annotation data Translate the reading to the target language.

  In this way, the various dictionaries used are converted into program logic and function as a program. Therefore, the reading dictionary is assigned to the map annotation through the program without referring to the various dictionaries. The kana is translated into a predetermined language. Thereby, since an external dictionary is not referred to, a large amount of map notes can be translated into a target language at high speed.

[Specific example of translation device operation]
Next, a specific example of translation processing in the translation apparatus of this embodiment described with reference to FIG. 1 will be described. The translation device of this embodiment is capable of translating a large amount of map notes at an ultra-high speed, but in order to explain the processing contents in a simple manner, it is necessary to specifically describe how one input data is processed. I will explain it.

  FIG. 4 shows a case where the translation apparatus shown in FIG. 1 processes input data with an area code “23 (Aichi Prefecture)”, an attribute number “138 (place name)”, and a map note “Kominato”. It is a figure for demonstrating the example of. As shown in FIG. 4, input data “23 (area code), 138 (attribute number), Kosugetsu (map note)” is prepared in the translation object data file 131 (step S1).

  Then, the reading kana giving program execution unit 132 executes the reading kana giving program formed by converting the reading kana dictionary into the program logic (step S2). The reading pseudonym assigning program reads the input data of the translation object data file 131 to form reading pseudonym data to which the reading pseudonym corresponding to the map annotation data in the input data is assigned. (Step S3).

  The reading kana data recorded in the reading kana data file 133 in step S3 is obtained by further adding the reading kana “Kodosori” to the input data as shown in step S3 of FIG. This kana data is commonly used when translating into any language. That is, the reading kana data in the reading kana data file 133 is commonly used by the translation programs of the respective languages.

  Next, the language-specific translation program formed by converting the language-specific translation dictionary into the program logic is executed by the language-specific translation program execution unit 134 (step S4). The language-specific translation program reads the kana data in the reading kana data file 133 and reads the language-specific translation data to which a character string (translation data) of a predetermined language corresponding to the reading kana in the reading kana data is given. This is formed and recorded in the language-specific translation data file 135 (step S5).

  The translation data recorded in the language-specific translation data file 135 in step S5 is obtained by further adding translation data of each language to the reading kana data as shown in step S5 of FIG. In this embodiment, since it is translated into four languages of Chinese (simplified), Chinese (traditional), English, and Korean, as shown in step S5 of FIG. Translation data for each of the four languages is formed.

  On the other hand, FIG. 5 is a diagram for explaining an outline of translation processing in a conventional translation apparatus. As shown in FIG. 5, in the conventional translation apparatus, when the input data similar to the example shown in FIG. 4 is translated, the language-specific translation program first converts the character string to be translated (in this example, the map Note) is decomposed into meaningful character string units using techniques such as morphological analysis. Then, each decomposed character string (or character) is translated using a translation dictionary.

  In the case of this conventional translation apparatus, it is necessary to refer to various dictionaries (databases) at the stage of morphological analysis and the stage of translation processing. As described above, the time required to access the dictionary in various ways occupies a large part of the time required for the translation process, and the translation process takes time.

  However, as can be seen from a comparison between FIG. 4 and FIG. 5, in the translation apparatus of this application, since an external dictionary (database) is not referred to at all, the translation process can be speeded up. Moreover, by assigning a reading kana to a map note, translation according to the correct reading kana becomes possible. Further, the reading kana data formed by adding the reading kana to the map annotation data is commonly used in the translation programs of the respective languages. For this reason, in the translation program of each language, it is not necessary to perform the process of giving a reading pseudonym to a map note.

[Summary of Processing Performed by Translation Device of Embodiment]
The processing performed by the translation apparatus of this embodiment is a preparatory process for creating a reading pseudonym assignment program and a language-specific translation program, and a map annotation is translated using the created reading pseudonym assignment program and language-specific translation program. It can be broadly divided into translation processing. The pre-preparation process does not need to be repeated unless the base reading kana dictionary or language-specific translation dictionary is changed. In addition, the translation processing performed in the translation apparatus of this embodiment is performed by first assigning reading kana to map annotation data to form reading kana data, and translating the reading kana of the formed reading kana data. It is divided into processing to do. In the following, each of the preliminary preparation process and the translation process performed by the translation apparatus of this embodiment will be described in detail.

[Preparation process]
FIG. 6 is a flowchart for explaining pre-preparation processing performed in the translation apparatus of this embodiment. As shown in FIG. 6, in the preparatory process, first, the reading kana giving program creation unit 112 converts the contents of each reading kana dictionary in the reading kana dictionary group 111 into program logic, and reads the reading kana. A grant program is created (step S21). In the process of step S21, each of the reading kana dictionaries used for the forward match, the backward match, and the partial match is converted into a program logic that replaces the reading kana by the longest match method.

  Next, the language-specific translation program creation unit 122 converts the contents of each translation dictionary in the language-specific translation dictionary group 121 into program logic to create a language-specific translation program (step S22). Also in the process of step S22, each of the translation dictionaries used for the forward match, the backward match, and the partial match is converted into a program logic that is replaced with the target translation language by the longest match method.

  Through these two processes, the reading pseudonym assignment program storage unit 113 stores the reading pseudonym assignment program, the language-specific translation program storage unit 123 stores the translation program for each language, and prepares the map annotation translation processing. Is arranged. The language-specific translation program storage unit 123 stores a plurality of language-specific translation programs.

  In this way, advance preparation before translation processing can be performed through the processing shown in FIG. Then, as described above, the advance preparation shown in FIG. 6 is executed when a change occurs in the reading kana dictionary of the reading kana dictionary group 111 or a change occurs in the translation dictionary of the language-specific translation dictionary group 121. That's fine. Of course, when only the reading kana dictionary of the reading kana dictionary group 111 is changed, only the process of step S21 shown in FIG. Further, when only the translation dictionary in the language-specific translation dictionary group 121 is changed, only the process of step S22 shown in FIG.

[Translation processing]
FIG. 7 is a flowchart for explaining a translation process performed by the translation apparatus of this embodiment. As shown in FIG. 7, the translation process includes a reading pseudonym assignment process (FIG. 7A) and a language-specific translation process (FIG. 7B). The reading pseudonym assignment process (FIG. 7A) is a process performed by the reading pseudonym assignment program executed by the reading pseudonym assignment program execution unit 132. The language-specific translation process (FIG. 7B) is a process performed by the language-specific translation program executed by the language-specific translation program execution unit 134.

  In the translation process, first, the reading pseudonym assignment program execution unit 132 executes the reading pseudonym assignment program, the translation object data file 131 is set as an input file, and the reading pseudonym data file 133 is set as an output file as shown in FIG. The kana adding process shown is performed. In the reading pseudonym assignment process, the reading pseudonym assignment program execution unit 132 sequentially reads the translation target data from the translation target data file 131 (step S31). The reading pseudonym assignment program execution unit 132 determines whether the translation target data has been read (whether reading of all the translation target data has been completed) (step S32).

  Assume that it is determined in the determination process of step S32 that the data to be translated has been read. In this case, the reading pseudonym assignment program execution unit 132 forms a reading pseudonym data by assigning a reading pseudonym to the read map annotation data of the data to be translated, and performs a process of writing this into the reading pseudonym data file 133. (Step S33). Thereafter, the processing from step S31 is repeated.

  If it is determined in step S32 that the reading of all translation handling data has been completed, the reading pseudonym assignment process shown in FIG.

  When the reading pseudonym assignment process by the reading pseudonym assignment program is completed, the language-specific translation program execution unit 134 executes the language-specific translation program, the reading pseudonym data file 133 is used as an input file, and the language-specific translation data file 135 is output as an output file. Then, the language-specific translation processing shown in FIG. In the language-specific translation process, the language-specific translation program execution unit 134 sequentially reads the reading kana data from the reading kana data file 133 (step S41). The language-specific translation program execution unit 134 determines whether reading kana data has been read (whether reading of all reading kana data has been completed) (step S42).

  Assume that it is determined in the determination process of step S42 that the reading kana data has been read. In this case, the language-specific translation program execution unit 134 assigns a character string (translation data) of a predetermined language to the reading kana of the read kana data and forms language-specific translation data, which is converted into the language-specific translation data. A process of writing to the file 135 is performed (step S43). Thereafter, the processing from step S41 is repeated.

  If it is determined in the determination process of step S42 that the reading kana data cannot be read (the reading of all reading kana data has been completed), the language-specific translation processing shown in FIG. The process shown in FIG. 7B is a process performed for each language by a language-specific translation program.

As described above, the translation process is performed through the reading pseudonym assignment process (FIG. 7A) and the language-specific translation process (FIG. 7B). In both of the reading pseudonym assignment process and the language-specific translation process, only the input file and the output file exist, and no reference database exists. That is, the reading kana dictionary and the translation dictionary are converted into program logic, which are a reading kana giving program and a language-specific translation program, respectively. For this reason, the assignment of a reading kana to a map note is processed so as to be completed only in the reading kana giving program, and the translation of the given reading kana to a predetermined language is completed only in the language-specific translation program. In this way, the translation apparatus according to this embodiment realizes extremely high-speed translation of a large number of map annotations.

[Effect of the embodiment]
According to the translation apparatus of the embodiment described above, a large amount of map notes can be translated significantly faster and more accurately than conventional machine translation apparatuses.

  Moreover, since the reading kana is given to the map note, and the given reading kana is translated, the translation according to the actual reading can be performed. There is no unnatural translation.

  Moreover, the reading kana data formed by adding the reading kana can be commonly used in the translation programs of the respective languages. That is, even when the map note is translated into a plurality of languages, it is not necessary to perform the process of forming the reading kana data redundantly by the translation program of each language.

  And when translating millions of map notes, it used to take weeks to translate, but it could be reduced to hours. Each language processing program realizes several tens of minutes of processing time.

[Modifications]
In the above-described embodiment, the map annotation data is described as being translated into four languages of Chinese (simplified), Chinese (traditional), English, and Korean. However, the present invention is not limited to this. Of course, it can be translated into other various languages. The combination of languages to be translated can be various combinations. Of course, it is also possible to translate into a single language.

  In the above-described embodiment, the case where the map annotation data is translated has been described as an example. However, the present invention is not limited to this. For example, the present invention can be applied to a case where a large number of noun phrases such as a phone book list, a customer list, and an address list are included, but a large number of noun phrases are translated.

  Further, in the above-described embodiment, it has been described that translation is performed on a reading pseudonym. However, it is not limited to this. For example, it is possible to perform translation more appropriately while comparing the translation target itself made up of hiragana, katakana, kanji, alphabet, numbers, and symbols with the created kana.

[Others]
As can be understood from the above description of the embodiment, the reading device's reading kana dictionary corresponds to each reading device dictionary in the reading device dictionary group of the translation device according to the embodiment. The translation dictionary of the translation device described in the above corresponds to each translation dictionary in the language-specific translation dictionary group of the translation device of the embodiment. The first program creation means of the translation device recited in the claims corresponds to the reading pseudonym assignment program creation unit 112 of the translation device according to the embodiment, and the second program creation means of the translation device according to the claims. Corresponds to the language-specific translation program creation unit 122 of the translation apparatus of this embodiment.

  Further, the reading device of the translation device described in the claims corresponds to the reading device of the translation device of the translation device of this embodiment, and the translation device of the translation device described in the claim This corresponds to the language-specific translation program execution unit 134 of the translation apparatus according to the embodiment.

  Moreover, the translation processing explained using FIG. 5 and FIG. 6 is one to which an embodiment of the translation method of the present invention is applied, and a program for executing the translation processing explained using FIG. 5 and FIG. Is applied with an embodiment of the translation program of the present invention. The functions of the reading-kana assignment program creating unit 112, the language-specific translation program creating unit 122, the reading-kana adding program execution unit 132, and the language-specific translation program execution unit 134 shown in FIG. It can also be realized as a function of the control unit 101 by a program.

  DESCRIPTION OF SYMBOLS 101 ... Control part, 102 ... Memory | storage device, 103 ... Operation part, 111 ... Reading kana dictionary group, 112 ... Reading kana provision program creation part, 113 ... Reading kana provision program storage part, 121 ... Translation dictionary group according to language, 122 ... Language-specific translation program creation unit, 123... Language-specific translation program storage unit, 131... Translation target data file, 132 ... reading-kana adding program execution unit, 133 ... reading-kana data file, 134. Translation data file by language

Claims (7)

A phonetic dictionary that associates strings with their phonetic kana,
First program creating means for converting a content of the reading-reading kana dictionary into program logic and creating a reading-kana adding program for adding a reading kana to an input character string;
A translation dictionary associating a reading kana with a character string of a predetermined language corresponding to the reading kana,
Second program creating means for converting the contents of the translation dictionary into program logic and creating a translation program for translating the input reading pseudonym into a predetermined language;
A reading pseudonym giving unit that executes the reading pseudonym giving program created by the first program creation unit and gives a reading pseudonym to a character string to be translated;
A translation apparatus comprising: a translation unit that executes the translation program created by the second program creation unit and translates the reading pseudonym assigned by the reading pseudonym granting unit into a target language. The translation device according to claim 1,
The translation apparatus characterized in that the second program creation means can convert the program logic into a program logic that matches a predetermined translation condition. A translation device according to claim 1 or 2, wherein
2. The translation apparatus according to claim 1, wherein the reading kana dictionary and the translation dictionary are made up of a plurality of dictionaries in which search word matching methods are different depending on forward matching, backward matching, and partial matching. The translation device according to claim 3,
The first program creation means converts each of the reading kana dictionary used for forward matching, backward matching, and partial matching into a program logic for replacing the reading kana by the longest matching method,
The second program creation means converts each of the translation dictionaries used for forward matching, backward matching, and partial matching into a program logic that replaces the target translation language with the longest matching method. Translation device to do. A translation device according to any one of claims 1, 2, 3, or 4,
The character string to be translated is related to a place, and in the reading kana conversion dictionary, corresponding to each character string, area information indicating a region to which the character string belongs and an attribute indicating an attribute of the character string Information is added,
The translation apparatus characterized in that the first program creation means converts the contents of the reading-reading kana conversion dictionary into program logic including the area information and the attribute information. The first program creating means converts the contents of a reading kana dictionary in which a character string and its reading kana are associated with each other into program logic, and creates a reading kana adding program for adding a reading kana to the input character string. 1 program creation process;
The second program creation means converts the contents of the translation dictionary in which the reading kana and the character string of the predetermined language corresponding to the reading kana are associated with each other into program logic, and translates the input reading kana into the predetermined language A second program creation step for creating a translation program to be performed;
The reading pseudonym assigning step executed by the reading pseudonym assigning unit to give the reading pseudonym to the character string to be translated;
A translation method comprising: a translation step executed by a translation unit for translating the translation program created in the second program creation step and translating the reading pseudonym assigned in the reading pseudonym assignment step into a target language . A computer mounted on an information processing apparatus comprising a reading kana dictionary that associates a character string with its reading kana, and a translation dictionary that associates the reading kana with a character string of a predetermined language corresponding to the reading kana The
First program creating means for converting a content of the reading-reading kana dictionary into program logic and creating a reading-kana adding program for adding a reading kana to an input character string;
Second program creating means for converting the contents of the translation dictionary into program logic and creating a translation program for translating the input reading pseudonym into a predetermined language;
A reading pseudonym giving unit that executes the reading pseudonym giving program created by the first program creation unit and gives a reading pseudonym to a character string to be translated;
A translation program that executes the translation program created by the second program creation means and functions as a translation means for translating the reading pseudonym given by the reading pseudonym giving means into a target language .

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