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 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.
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Keyword(in English) Term extraction / Molecular biology / Support vector machine
Paper # NLC2002-36
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Committee NLC
Conference Date 2002/7/9(1days)
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Registration To Natural Language Understanding and Models of Communication (NLC)
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