1996 Fiscal Year Final Research Report Summary
Lexical Information Acquisition Using Accent Knowedge
| Project/Area Number |
07680383
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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 of Agriculture and Technology |
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Principal Investigator |
KOTANI Yoshiyuki Tokyo University of Agriculture and Technology Faculty of Engineer Professor, 工学部, 教授 (20111627)
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| Co-Investigator(Kenkyū-buntansha) |
NOSE Takashi Tokyo University of Agriculture and Technology Faculty of Engineer Assistant Pro, 工学部, 助手 (30262231)
INUI Nobuo Tokyo University of Agriculture and Technology Faculty of Engineer Assistant Pro, 工学部, 助手 (20236384)
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| Project Period (FY) |
1995 – 1996
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| Keywords | Accent / Morphology / Unknown Word / Corpus / Tagging / Probability / HMM / Language Acquisition |
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| Research Abstract |
In natural language processing, to handle unknown words is not avoided. The aim of our research is to solve this problem in the morphological analysis, especially using accent information. Our method make it possible to estimate extract unknown words from texts and acquire its part of speech information and inflection using the phonetic information. In an extraction of unknown words from written sentences, kanjis and kanas play an important role. Though in phonetic interaction, there are not such information, we can use accent information. We designed a mechanism which aim is to acquire unknown words using accent information.
The flow of our system is :
1.analyze sentences with accent information morphologically
2.estimate unknown words' part of speech and inflection.
Our method below is a novel method in the meaning that it acquires unknown words' information in honetics and is significant in cognitive psychology which tries to understand the process of language learning.
In this research, firstly, we built the morphological analyzer for kana sentences using regulations of accent information. To acquiring regulations automatically, we studied on the morphological analysis using the GMDH neural network. Also we studied on the HMM morphological analyzer and technique acquiring probability information from non-tagged corpus for it and the extraction of strength between parts of speech or words. As a relative research, we studied on phonetics in music.
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