Computational Linguistics
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
An estimate of referent of noun phrases in Japanese sentences
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Efficient support vector classifiers for named entity recognition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Improving machine learning approaches to coreference resolution
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A mention-synchronous coreference resolution algorithm based on the Bell tree
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Machine learning for coreference resolution: from local classification to global ranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Automatic construction of nominal case frames and its application to indirect anaphora resolution
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Improving noun phrase coreference resolution by matching strings
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
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We present a knowledge-rich approach to Japanese coreference resolution. In Japanese, proper noun coreference and common noun coreference occupy a central position in coreference relations. To improve coreference resolution for such language, wide-coverage knowledge of synonyms is required. We first acquire knowledge of synonyms from large raw corpus and dictionary definition sentences, and resolve coreference relations based on the knowledge. Furthermore, to boost the performance of coreference resolution, we integrate bridging reference resolution system into coreference resolver.