Presentation | 2013-10-19 Knowledge Acquisition by Hierarchical Structured Semantic Space Model. Akinori TAKADA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | In this article I propose "Hierarchical Structured Semantic Space Model" as a method to construct natural language based question-answering system. In rule-based natural language processing, input sentences are needed to be translated into certain types of meta-form, and there are some problems as below: (1) how we translate sentences like "I think that ..." or "He believes that ...", (2) how we manage contradictions in sentences input by two persons, (3) how we get logically consistent knowledge from various sentences with miscellaneous types. In this proposed method, individually separated semantic spaces having hierarchical structure in virtually placed main system are constructed, and meta-form data in lower semantic space such as "fact" or "logic" are appropriately transformed and brought up into upper semantic space. Further problems are discussed along with examples of implemented system. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | natural language processing / question-answering system / semantic space / knowledge acquisition |
Paper # | TL2013-46 |
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Committee | TL |
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Conference Date | 2013/10/12(1days) |
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Registration To | Thought and Language (TL) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Knowledge Acquisition by Hierarchical Structured Semantic Space Model. |
Sub Title (in English) | |
Keyword(1) | natural language processing |
Keyword(2) | question-answering system |
Keyword(3) | semantic space |
Keyword(4) | knowledge acquisition |
1st Author's Name | Akinori TAKADA |
1st Author's Affiliation | Faculty of Letters, Department of Communication Studies, Ferris University() |
Date | 2013-10-19 |
Paper # | TL2013-46 |
Volume (vol) | vol.113 |
Number (no) | 253 |
Page | pp.pp.- |
#Pages | 6 |
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