Sense-tagging Chinese corpus

Authors:
Hsin-Hsi Chen;Chi-Ching Lin
Affiliations:
National Taiwan University, Taipei, Taiwan;National Taiwan University, Taipei, Taiwan
Venue:
CLPW '00 Proceedings of the second workshop on Chinese language processing: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 12
Year:
2000

Citing 4
Cited 4

Resolving ambiguity for cross-language retrieval

Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Foundations of statistical natural language processing

Foundations of statistical natural language processing
Introduction to the special issue on word sense disambiguation: the state of the art

Computational Linguistics - Special issue on word sense disambiguation
Integrating multiple knowledge sources to disambiguate word sense: an exemplar-based approach

ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics

Construction of a Chinese-English WordNet and its application to CLIR

IRAL '00 Proceedings of the fifth international workshop on on Information retrieval with Asian languages
Building a Chinese-English wordnet for translingual applications

ACM Transactions on Asian Language Information Processing (TALIP)
Considerations of linking WordNet with MRD

COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Combining contextual and structural information for supersense tagging of chinese unknown words

CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

Contextual information and the mapping from WordNet synsets to Cilin sense tags deal with word sense disambiguation. The average performance is 63.36% when small categories are used, and 1, 2 and 3 candidates are proposed for low, middle and high ambiguous words. The performance of tagging unknown words is 34.35%, which is much better than that of baseline mode. The sense tagger achieves the performance of 76.04%, when unambiguous, ambiguous, and unknown words are tagged.