@inproceedings{zou-etal-2006-evaluation,
title = "Evaluation of Stop Word Lists in {C}hinese Language",
author = "Zou, Feng and
Wang, Fu Lee and
Deng, Xiaotie and
Han, Song",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Gangemi, Aldo and
Maegaard, Bente and
Mariani, Joseph and
Odijk, Jan and
Tapias, Daniel",
booktitle = "Proceedings of the Fifth International Conference on Language Resources and Evaluation ({LREC}{'}06)",
month = may,
year = "2006",
address = "Genoa, Italy",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2006/pdf/273_pdf.pdf",
abstract = "In modern information retrieval systems, effective indexing can be achieved by removal of stop words. Till now many stop word lists have been developed for English language. However, no standard stop word list has been constructed for Chinese language yet. With the fast development of information retrieval in Chinese language, exploring the evaluation of Chinese stop word lists becomes critical. In this paper, to save the time and release the burden of manual comparison, we propose a novel stop word list evaluation method with a mutual information-based Chinese segmentation methodology. Experiments have been conducted on training texts taken from a recent international Chinese segmentation competition. Results show that effective stop word lists can improve the accuracy of Chinese segmentation significantly.",
}
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<abstract>In modern information retrieval systems, effective indexing can be achieved by removal of stop words. Till now many stop word lists have been developed for English language. However, no standard stop word list has been constructed for Chinese language yet. With the fast development of information retrieval in Chinese language, exploring the evaluation of Chinese stop word lists becomes critical. In this paper, to save the time and release the burden of manual comparison, we propose a novel stop word list evaluation method with a mutual information-based Chinese segmentation methodology. Experiments have been conducted on training texts taken from a recent international Chinese segmentation competition. Results show that effective stop word lists can improve the accuracy of Chinese segmentation significantly.</abstract>
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%0 Conference Proceedings
%T Evaluation of Stop Word Lists in Chinese Language
%A Zou, Feng
%A Wang, Fu Lee
%A Deng, Xiaotie
%A Han, Song
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Gangemi, Aldo
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Tapias, Daniel
%S Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)
%D 2006
%8 May
%I European Language Resources Association (ELRA)
%C Genoa, Italy
%F zou-etal-2006-evaluation
%X In modern information retrieval systems, effective indexing can be achieved by removal of stop words. Till now many stop word lists have been developed for English language. However, no standard stop word list has been constructed for Chinese language yet. With the fast development of information retrieval in Chinese language, exploring the evaluation of Chinese stop word lists becomes critical. In this paper, to save the time and release the burden of manual comparison, we propose a novel stop word list evaluation method with a mutual information-based Chinese segmentation methodology. Experiments have been conducted on training texts taken from a recent international Chinese segmentation competition. Results show that effective stop word lists can improve the accuracy of Chinese segmentation significantly.
%U http://www.lrec-conf.org/proceedings/lrec2006/pdf/273_pdf.pdf
Markdown (Informal)
[Evaluation of Stop Word Lists in Chinese Language](http://www.lrec-conf.org/proceedings/lrec2006/pdf/273_pdf.pdf) (Zou et al., LREC 2006)
ACL
- Feng Zou, Fu Lee Wang, Xiaotie Deng, and Song Han. 2006. Evaluation of Stop Word Lists in Chinese Language. In Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06), Genoa, Italy. European Language Resources Association (ELRA).