Mansouri et al., 2008 - Google Patents
A new fuzzy support vector machine method for named entity recognitionMansouri et al., 2008
- Document ID
- 9493717738790102749
- Author
- Mansouri A
- Affendy L
- Mamat A
- Publication year
- Publication venue
- 2008 International Conference on Computer Science and Information Technology
External Links
Snippet
Recognizing and extracting exact name entities, like Persons, Locations, Organizations, Dates and Times are very useful to mining information from electronics resources and text. Learning to extract these types of data is called Named Entity Recognition (NER) task …
- 238000010801 machine learning 0 abstract description 11
Classifications
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- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30634—Querying
- G06F17/30657—Query processing
- G06F17/30675—Query execution
- G06F17/30684—Query execution using natural language analysis
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- G06F17/2765—Recognition
- G06F17/2775—Phrasal analysis, e.g. finite state techniques, chunking
- G06F17/278—Named entity recognition
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- G06F17/271—Syntactic parsing, e.g. based on context-free grammar [CFG], unification grammars
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- G—PHYSICS
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- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/68—Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
- G06K9/6807—Dividing the references in groups prior to recognition, the recognition taking place in steps; Selecting relevant dictionaries
- G06K9/6842—Dividing the references in groups prior to recognition, the recognition taking place in steps; Selecting relevant dictionaries according to the linguistic properties, e.g. English, German
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