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".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Neste artigo, a resolução eficaz da anáfora definida chinesa é abordada usando o aprendizado de peso de recursos e a aquisição de conhecimento baseada na Web. A medida de relevância apresentada é baseada na ponderação baseada na entropia na seleção de candidatos antecedentes. O modelo de aquisição de conhecimento visa extrair mais características semânticas, como gênero, número e compatibilidade semântica, por meio do emprego de múltiplos recursos e mineração na Web. A resolução é justificada com um corpus real e comparada com um modelo baseado em classificação. Os resultados experimentais mostram que nossa abordagem produz uma taxa de sucesso de 72.5% em 426 instâncias anafóricas. Em comparação com uma abordagem baseada na classificação geral, o desempenho melhorou 4.7%.
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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},}
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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 -