Technical term recognition with semi-supervised learning using hierarchical bayesian language models

Ryo Fujii, Akito Sakurai

研究成果: Conference contribution

抄録

To recognize technical term, term dictionaries or tagged corpora are required, but it will take much cost to compile them. Moreover, the terms may have several representations and new terms may be developed, which complicates the problem further, that is, a simple dictionary building can't solve the problem. In this research, to reduce the cost of creating dictionaries, we aimed at building a system that learns to recognize terminology from small tagged corpus using semi-supervised learning. We solved the problem by combining a tag level language model and a character level language model based on HPYLM. We performed experiments on recognition of biomedical terms. In supervised learning, we achived 65% F-measure which is 8% points behind the best existing system that utilizes many domain specific heuristics. In semi-supervised learning, we could keep the accuracy against reduction of supervised data better than exisiting methods.

本文言語English
ホスト出版物のタイトルNatural Language Processing and Information Systems - 17th International Conference on Applications of Natural Language to Information Systems, NLDB 2012, Proceedings
ページ327-332
ページ数6
DOI
出版ステータスPublished - 2012
外部発表はい
イベント17th International Conference on Applications of Natural Language to Information Systems, NLDB 2012 - Groningen, Netherlands
継続期間: 2012 6 262012 6 28

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
7337 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other17th International Conference on Applications of Natural Language to Information Systems, NLDB 2012
国/地域Netherlands
CityGroningen
Period12/6/2612/6/28

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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