Kernel methods for relation extraction
The Journal of Machine Learning Research
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
The Penn Chinese TreeBank: Phrase structure annotation of a large corpus
Natural Language Engineering
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
Coreference resolution using competition learning approach
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
A computational approach to zero-pronouns in Spanish
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Japanese zero pronoun resolution based on ranking rules and machine learning
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Pronominal anaphora resolution in chinese
Pronominal anaphora resolution in chinese
A study on convolution kernels for shallow semantic parsing
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Extracting relations with integrated information using kernel methods
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Kernel-based pronoun resolution with structured syntactic knowledge
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Exploiting syntactic patterns as clues in zero-anaphora resolution
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A twin-candidate model for learning-based anaphora resolution
Computational Linguistics
Exploiting constituent dependencies for tree kernel-based semantic relation extraction
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
A fully-lexicalized probabilistic model for Japanese zero anaphora resolution
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Employing the centering theory in pronoun resolution from the semantic perspective
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
A two-step zero pronoun resolution by reducing candidate cardinality
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
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This paper proposes a unified framework for zero anaphora resolution, which can be divided into three sub-tasks: zero anaphor detection, anaphoricity determination and antecedent identification. In particular, all the three sub-tasks are addressed using tree kernel-based methods with appropriate syntactic parse tree structures. Experimental results on a Chinese zero anaphora corpus show that the proposed tree kernel-based methods significantly outperform the feature-based ones. This indicates the critical role of the structural information in zero anaphora resolution and the necessity of tree kernel-based methods in modeling such structural information. To our best knowledge, this is the first systematic work dealing with all the three sub-tasks in Chinese zero anaphora resolution via a unified framework. Moreover, we release a Chinese zero anaphora corpus of 100 documents, which adds a layer of annotation to the manually-parsed sentences in the Chinese Treebank (CTB) 6.0.