| Project/Area Number |
14580409
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Tokyo University Agriculture And Technology |
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Principal Investigator |
INUI Nobuo Tokyo University Agriculture And Technology, Faculty of Technology, Assistant Professor, 工学部, 助手 (20236384)
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| Co-Investigator(Kenkyū-buntansha) |
KOTANI Yosiyuki Tokyo University Agriculture And Technology, Faculty of Technology, Professor, 工学部, 教授 (20111627)
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| Project Period (FY) |
2002 – 2003
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| Project Status |
Completed (Fiscal Year 2003)
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| Budget Amount *help |
¥3,600,000 (Direct Cost: ¥3,600,000)
Fiscal Year 2003: ¥1,400,000 (Direct Cost: ¥1,400,000)
Fiscal Year 2002: ¥2,200,000 (Direct Cost: ¥2,200,000)
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| Keywords | Natural language dialogue / Adverb / Case-based / Parsing / Morphological analyzer / Face-mark / Sentence generator / Keyword-matching / 時間副詞 / 程度副詞 / 類似性 |
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| Research Abstract |
The aim of this research is to implement a topic-free natural language dialogue system that provides environments to communicate human with computers. Since our developing method uses case-database to generate computer's utterances, the topic-free natural language dialogue system can supply user's desirable information by embedding a function of revising the case-database in real-time to the system.
In this research, we focus on the use of adverbs in such a natural language dialogue. Many adverbs reflect user's subjective images about his situation. When an adverb is used in unnatural way, human cannot interpret the sentence correctly. In this research, we analyzed usages of time-adverb and grade-adverb. Time-adverb usually express the subjectivity of a speaker for time recognition. We used an extended Hornstein model and experimented it on a natural language dialogue system. Grade-adverb usually modifies adjectives but sometimes modifies verbs. In this case, adjectives are omitted. We analyzed this phenomenon and made a model of estimating omitted adjectives.
A matching between an inputted sentence and case-data is evaluated by keywords and structures of sentences. In the keyword-matching, our system extract important terms like a noun, a verb and an adjective. In the structural-matching, the importance of terms is judged by the position on the syntactic tree. To implement our system, we developed a morphological analyzer and a syntactic parser, which are efficient to extract information for our dialogue system. In addition, we developed a sentence generator using a slot-method.
express user intention, we researched on face-marks that have an equivalent character to adverbs. Though the mechanism of generating face-marks has not been known yet, we modeled the transition of emotions and estimating a face-mark that is suitable for the current sentence by neural network.
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