Presentation | 2002/7/9 Analysis of Machine Learning Model for Technical Term Extraction in Biological Science Papers Koichi Takeuchi, Nigel Collier, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | This paper explores the use of Support Vector Machines (SVMs) for an extended named entity task. We investigate the identification and classification of technical terms in the molecular biology domain and contrast this to results obtained for traditional NE recognition on the MUC-6 data set. Furthermore we compare the performance of the SVM model to a standard HMM bigram model. Results show that the SVM utilizing a rich feature set of a ±3 context window and orthographic features had a significant performance advantage on both the MUC-6 and molecular biology data sets. From the results, the paper show what kind of parameter sets are important for constructing the best extraction model. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Term extraction / Molecular biology / Support vector machine |
Paper # | NLC2002-36 |
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Conference Information | |
Committee | NLC |
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Conference Date | 2002/7/9(1days) |
Place (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Natural Language Understanding and Models of Communication (NLC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Analysis of Machine Learning Model for Technical Term Extraction in Biological Science Papers |
Sub Title (in English) | |
Keyword(1) | Term extraction |
Keyword(2) | Molecular biology |
Keyword(3) | Support vector machine |
1st Author's Name | Koichi Takeuchi |
1st Author's Affiliation | National Institute of Informatics() |
2nd Author's Name | Nigel Collier |
2nd Author's Affiliation | National Institute of Informatics |
Date | 2002/7/9 |
Paper # | NLC2002-36 |
Volume (vol) | vol.102 |
Number (no) | 200 |
Page | pp.pp.- |
#Pages | 6 |
Date of Issue |