Improving Xtract for Chinese collocation extraction

Qin Lu; Yin Li; Ruifeng Xu

doi:10.1109/NLPKE.2003.1275925

Improving Xtract for Chinese collocation extraction

Qin Lu, Yin Li, Ruifeng Xu

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

10 Citations (Scopus)

Abstract

This paper presents a system which extracts word-based bi-gram and n-gram collocation information from a 60MB corpus and then locates bi-gram pairs using Strength and Spread as defined in the Xtract system. In order for Xtract to work effectively with Chinese, we have re-adjusted the parameters. To obtain a higher recall rate, we have modified the algorithm to identify collocations with low-frequency of occurrence, a method which works particularly well in the case of bi-grams in which one word is high-frequency and the other low-frequency. In preliminary experiments, our system extracts bi-gram collocations with a precision of 61%: an 8% improvement over the direct use Smadja' Xtract on Chinese. Further. \\'e have improved the recall rate by 4.5% while extracting multi-word collocations with 92% precision.

Original language	English
Title of host publication	NLP-KE 2003 - 2003 International Conference on Natural Language Processing and Knowledge Engineering, Proceedings
Publisher	IEEE
Pages	333-338
Number of pages	6
ISBN (Electronic)	0780379020, 9780780379022
DOIs	https://doi.org/10.1109/NLPKE.2003.1275925
Publication status	Published - 1 Jan 2003
Event	International Conference on Natural Language Processing and Knowledge Engineering, NLP-KE 2003 - Beijing Media Center, Beijing, China Duration: 26 Oct 2003 → 29 Oct 2003

Conference

Conference	International Conference on Natural Language Processing and Knowledge Engineering, NLP-KE 2003
Country/Territory	China
City	Beijing
Period	26/10/03 → 29/10/03

Keywords

Chinese collocation
Information
Statistical modeling

ASJC Scopus subject areas

Artificial Intelligence
Computational Theory and Mathematics
Software

More information

10.1109/NLPKE.2003.1275925

Cite this

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title = "Improving Xtract for Chinese collocation extraction",

abstract = "This paper presents a system which extracts word-based bi-gram and n-gram collocation information from a 60MB corpus and then locates bi-gram pairs using Strength and Spread as defined in the Xtract system. In order for Xtract to work effectively with Chinese, we have re-adjusted the parameters. To obtain a higher recall rate, we have modified the algorithm to identify collocations with low-frequency of occurrence, a method which works particularly well in the case of bi-grams in which one word is high-frequency and the other low-frequency. In preliminary experiments, our system extracts bi-gram collocations with a precision of 61%: an 8% improvement over the direct use Smadja' Xtract on Chinese. Further. \\'e have improved the recall rate by 4.5% while extracting multi-word collocations with 92% precision.",

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Lu, Q, Li, Y & Xu, R 2003, Improving Xtract for Chinese collocation extraction. in NLP-KE 2003 - 2003 International Conference on Natural Language Processing and Knowledge Engineering, Proceedings., 1275925, IEEE, pp. 333-338, International Conference on Natural Language Processing and Knowledge Engineering, NLP-KE 2003, Beijing, China, 26/10/03. https://doi.org/10.1109/NLPKE.2003.1275925

Improving Xtract for Chinese collocation extraction. / Lu, Qin; Li, Yin; Xu, Ruifeng.
NLP-KE 2003 - 2003 International Conference on Natural Language Processing and Knowledge Engineering, Proceedings. IEEE, 2003. p. 333-338 1275925.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Improving Xtract for Chinese collocation extraction

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AU - Xu, Ruifeng

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N2 - This paper presents a system which extracts word-based bi-gram and n-gram collocation information from a 60MB corpus and then locates bi-gram pairs using Strength and Spread as defined in the Xtract system. In order for Xtract to work effectively with Chinese, we have re-adjusted the parameters. To obtain a higher recall rate, we have modified the algorithm to identify collocations with low-frequency of occurrence, a method which works particularly well in the case of bi-grams in which one word is high-frequency and the other low-frequency. In preliminary experiments, our system extracts bi-gram collocations with a precision of 61%: an 8% improvement over the direct use Smadja' Xtract on Chinese. Further. \\'e have improved the recall rate by 4.5% while extracting multi-word collocations with 92% precision.

AB - This paper presents a system which extracts word-based bi-gram and n-gram collocation information from a 60MB corpus and then locates bi-gram pairs using Strength and Spread as defined in the Xtract system. In order for Xtract to work effectively with Chinese, we have re-adjusted the parameters. To obtain a higher recall rate, we have modified the algorithm to identify collocations with low-frequency of occurrence, a method which works particularly well in the case of bi-grams in which one word is high-frequency and the other low-frequency. In preliminary experiments, our system extracts bi-gram collocations with a precision of 61%: an 8% improvement over the direct use Smadja' Xtract on Chinese. Further. \\'e have improved the recall rate by 4.5% while extracting multi-word collocations with 92% precision.

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Improving Xtract for Chinese collocation extraction

Abstract

Conference

Keywords

ASJC Scopus subject areas

More information

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