標題: Improving Definite Anaphora Resolution by Effective Weight Learning and Web-Based Knowledge Acquisition
作者: Wu, Dian-Song
Liang, Tyne
資訊工程學系
Department of Computer Science
關鍵字: definite anaphora resolution;feature weight learning;Web mining
公開日期: 1-Mar-2011
摘要: 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%.
URI: http://dx.doi.org/10.1587/transinf.E94.D.535
http://hdl.handle.net/11536/9163
ISSN: 0916-8532
DOI: 10.1587/transinf.E94.D.535
期刊: IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
Volume: E94D
Issue: 3
起始頁: 535
結束頁: 541
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