Named entity recognition based on bilingual co-training

Yegang Li; Heyan Huang; Xingjian Zhao; Shumin Shi

doi:10.1007/978-3-642-45185-0_50

Named entity recognition based on bilingual co-training

Yegang Li, Heyan Huang^*, Xingjian Zhao, Shumin Shi

^*Corresponding author for this work

School of Computer Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

Named entity recognition (NER) is a very important task in natural language processing (NLP). In this paper we present a semi-supervised approach to extract bilingual named entity, starting from a bilingual corpus where the named entities are extracted independently for each language. Then a bilingual co-training algorithm is used to improve the named entity annotation quality, and iterative process is applied to extract named entity pairs with higher bilingual conformity ratio. This leads to a significant improvement of the monolingual named entity annotation quality for both languages. Experimental result shows that the annotation quality of Chinese NE is improved from 87.17 to 88.28, and improved 80.37 to 81.76 of English NE in F-measure.

Original language	English
Title of host publication	Chinese Lexical Semantics - 14th Workshop, CLSW 2013, Revised Selected Papers
Pages	480-489
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-45185-0_50
Publication status	Published - 2013
Event	14th Workshop on Chinese Lexical Semantics, CLSW 2013 - Zhengzhou, China Duration: 10 May 2013 → 12 May 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8229 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th Workshop on Chinese Lexical Semantics, CLSW 2013
Country/Territory	China
City	Zhengzhou
Period	10/05/13 → 12/05/13

Keywords

bilingual co-training
named entity recognition
natural language processing

Access to Document

10.1007/978-3-642-45185-0_50

Cite this

Li, Y., Huang, H., Zhao, X., & Shi, S. (2013). Named entity recognition based on bilingual co-training. In Chinese Lexical Semantics - 14th Workshop, CLSW 2013, Revised Selected Papers (pp. 480-489). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8229 LNAI). https://doi.org/10.1007/978-3-642-45185-0_50

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title = "Named entity recognition based on bilingual co-training",

abstract = "Named entity recognition (NER) is a very important task in natural language processing (NLP). In this paper we present a semi-supervised approach to extract bilingual named entity, starting from a bilingual corpus where the named entities are extracted independently for each language. Then a bilingual co-training algorithm is used to improve the named entity annotation quality, and iterative process is applied to extract named entity pairs with higher bilingual conformity ratio. This leads to a significant improvement of the monolingual named entity annotation quality for both languages. Experimental result shows that the annotation quality of Chinese NE is improved from 87.17 to 88.28, and improved 80.37 to 81.76 of English NE in F-measure.",

keywords = "bilingual co-training, named entity recognition, natural language processing",

author = "Yegang Li and Heyan Huang and Xingjian Zhao and Shumin Shi",

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Li, Y, Huang, H, Zhao, X & Shi, S 2013, Named entity recognition based on bilingual co-training. in Chinese Lexical Semantics - 14th Workshop, CLSW 2013, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8229 LNAI, pp. 480-489, 14th Workshop on Chinese Lexical Semantics, CLSW 2013, Zhengzhou, China, 10/05/13. https://doi.org/10.1007/978-3-642-45185-0_50

Named entity recognition based on bilingual co-training. / Li, Yegang; Huang, Heyan; Zhao, Xingjian et al.
Chinese Lexical Semantics - 14th Workshop, CLSW 2013, Revised Selected Papers. 2013. p. 480-489 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8229 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Zhao, Xingjian

AU - Shi, Shumin

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N2 - Named entity recognition (NER) is a very important task in natural language processing (NLP). In this paper we present a semi-supervised approach to extract bilingual named entity, starting from a bilingual corpus where the named entities are extracted independently for each language. Then a bilingual co-training algorithm is used to improve the named entity annotation quality, and iterative process is applied to extract named entity pairs with higher bilingual conformity ratio. This leads to a significant improvement of the monolingual named entity annotation quality for both languages. Experimental result shows that the annotation quality of Chinese NE is improved from 87.17 to 88.28, and improved 80.37 to 81.76 of English NE in F-measure.

AB - Named entity recognition (NER) is a very important task in natural language processing (NLP). In this paper we present a semi-supervised approach to extract bilingual named entity, starting from a bilingual corpus where the named entities are extracted independently for each language. Then a bilingual co-training algorithm is used to improve the named entity annotation quality, and iterative process is applied to extract named entity pairs with higher bilingual conformity ratio. This leads to a significant improvement of the monolingual named entity annotation quality for both languages. Experimental result shows that the annotation quality of Chinese NE is improved from 87.17 to 88.28, and improved 80.37 to 81.76 of English NE in F-measure.

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Named entity recognition based on bilingual co-training

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