The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dalam makalah ini, resolusi anafora pasti Cina yang berkesan ditangani dengan menggunakan pembelajaran berat ciri dan pemerolehan pengetahuan berasaskan Web. Pengukuran salience yang dibentangkan adalah berdasarkan pemberat berasaskan entropi pada pemilihan calon anteseden. Model pemerolehan pengetahuan bertujuan untuk mengekstrak lebih banyak ciri semantik, seperti jantina, nombor, dan keserasian semantik dengan menggunakan pelbagai sumber dan perlombongan Web. Resolusi itu dibenarkan dengan korpus sebenar dan dibandingkan dengan model berasaskan klasifikasi. Keputusan eksperimen menunjukkan bahawa pendekatan kami menghasilkan 72.5% kadar kejayaan pada 426 kejadian anafora. Berbanding dengan pendekatan berasaskan klasifikasi umum, prestasi meningkat sebanyak 4.7%.
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Salinan
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
Salinan
@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},}
Salinan
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 -