Improving Definite Anaphora Resolution by Effective Weight Learning and Web-Based Knowledge Acquisition
Summary :
In this paper, effective Chinese definite anaphora resolution is addressed by using feature weight learning and Web-based knowledge acquisition. The presented salience measurement is based on entropy-based weighting on selecting antecedent candidates. The knowledge acquisition model is aimed to extract more semantic features, such as gender, number, and semantic compatibility by employing multiple resources and Web mining. The resolution is justified with a real corpus and compared with a classification-based model. Experimental results show that our approach yields 72.5% success rate on 426 anaphoric instances. In comparison with a general classification-based approach, the performance is improved by 4.7%.
- Publication
- IEICE TRANSACTIONS on Information Vol.E94-D No.3 pp.535-541
- Publication Date
- 2011/03/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E94.D.535
- Type of Manuscript
- Special Section PAPER (Special Section on Data Engineering)
- Category
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Dian-Song WU, Tyne LIANG, "Improving Definite Anaphora Resolution by Effective Weight Learning and Web-Based Knowledge Acquisition" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 3, pp. 535-541, March 2011, doi: 10.1587/transinf.E94.D.535.
Abstract: In this paper, effective Chinese definite anaphora resolution is addressed by using feature weight learning and Web-based knowledge acquisition. The presented salience measurement is based on entropy-based weighting on selecting antecedent candidates. The knowledge acquisition model is aimed to extract more semantic features, such as gender, number, and semantic compatibility by employing multiple resources and Web mining. The resolution is justified with a real corpus and compared with a classification-based model. Experimental results show that our approach yields 72.5% success rate on 426 anaphoric instances. In comparison with a general classification-based approach, the performance is improved by 4.7%.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.535/_p
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@ARTICLE{e94-d_3_535,
author={Dian-Song WU, Tyne LIANG, },
journal={IEICE TRANSACTIONS on Information},
title={Improving Definite Anaphora Resolution by Effective Weight Learning and Web-Based Knowledge Acquisition},
year={2011},
volume={E94-D},
number={3},
pages={535-541},
abstract={In this paper, effective Chinese definite anaphora resolution is addressed by using feature weight learning and Web-based knowledge acquisition. The presented salience measurement is based on entropy-based weighting on selecting antecedent candidates. The knowledge acquisition model is aimed to extract more semantic features, such as gender, number, and semantic compatibility by employing multiple resources and Web mining. The resolution is justified with a real corpus and compared with a classification-based model. Experimental results show that our approach yields 72.5% success rate on 426 anaphoric instances. In comparison with a general classification-based approach, the performance is improved by 4.7%.},
keywords={},
doi={10.1587/transinf.E94.D.535},
ISSN={1745-1361},
month={March},}
Copy
TY - JOUR
TI - Improving Definite Anaphora Resolution by Effective Weight Learning and Web-Based Knowledge Acquisition
T2 - IEICE TRANSACTIONS on Information
SP - 535
EP - 541
AU - Dian-Song WU
AU - Tyne LIANG
PY - 2011
DO - 10.1587/transinf.E94.D.535
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2011
AB - In this paper, effective Chinese definite anaphora resolution is addressed by using feature weight learning and Web-based knowledge acquisition. The presented salience measurement is based on entropy-based weighting on selecting antecedent candidates. The knowledge acquisition model is aimed to extract more semantic features, such as gender, number, and semantic compatibility by employing multiple resources and Web mining. The resolution is justified with a real corpus and compared with a classification-based model. Experimental results show that our approach yields 72.5% success rate on 426 anaphoric instances. In comparison with a general classification-based approach, the performance is improved by 4.7%.
ER -
English
