Presentation | 2011-10-10 Comparison between statistical-learning-based system and rule-based system on biomedical term extraction task Shosaburo MINAMOTO, Koichi TAKEUCHI, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | We compare the term extraction system to extract technical terms automatically in texts. The methods of comparative target are based on statistical-learning-based model and rule-based model. In comparison, since the text data identified term on infection exists, we use this data as correct answer data. In statistical-learning-based model, we build the term extraction system by learning by CRF based on correct answer data. And in rule-based model, we use the extraction system using SRL as a rule-based word extraction language. We experimented in term extraction, and showed that it is good to perform term extraction by statical-learning based model when there are many correct answer data, and by rule-based model when texts depend on some fields. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Term extraction / Statistical-learning-based model / Rule-based model |
Paper # | TL2011-31,NLC2011-28 |
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Committee | TL |
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Conference Date | 2011/10/3(1days) |
Place (in Japanese) | (See Japanese page) |
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Paper Information | |
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) | Comparison between statistical-learning-based system and rule-based system on biomedical term extraction task |
Sub Title (in English) | |
Keyword(1) | Term extraction |
Keyword(2) | Statistical-learning-based model |
Keyword(3) | Rule-based model |
1st Author's Name | Shosaburo MINAMOTO |
1st Author's Affiliation | Graduate School of Natural Science and Technology, Okayama University() |
2nd Author's Name | Koichi TAKEUCHI |
2nd Author's Affiliation | Graduate School of Natural Science and Technology, Okayama University |
Date | 2011-10-10 |
Paper # | TL2011-31,NLC2011-28 |
Volume (vol) | vol.111 |
Number (no) | 227 |
Page | pp.pp.- |
#Pages | 5 |
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