The nature of statistical learning theory
The nature of statistical learning theory
Parts, wholes, and part-whole relations: the prospects of mereotopology
Data & Knowledge Engineering - Special issue on modeling parts and wholes
Analysis of part-whole relation and subsumption in the medical domain
Data & Knowledge Engineering - Special issue on modeling parts and wholes
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Improving machine learning approaches to coreference resolution
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Comparing Knowledge Sources for Nominal Anaphora Resolution
Computational Linguistics
Using the web in machine learning for other-anaphora resolution
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Automatic Discovery of Part-Whole Relations
Computational Linguistics
Semantic tagging for resolution of indirect anaphora
SigDIAL '06 Proceedings of the 7th SIGdial Workshop on Discourse and Dialogue
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This paper proposes an other-anaphora resolution approach in bio-medical texts. It utilizes automatically mined patterns to discover the semantic relation between an anaphor and a candidate antecedent. The knowledge from lexical patterns is incorporated in a machine learning framework to perform anaphora resolution. The experiments show that machine learning approach combined with the auto-mined knowledge is effective for other-anaphora resolution in the biomedical domain. Our system with auto-mined patterns gives an accuracy of 56.5%., yielding 16.2% improvement against the baseline system without pattern features, and 9% improvement against the system using manually designed patterns.