The nature of statistical learning theory
The nature of statistical learning theory
Japanese discourse and the process of centering
Computational Linguistics
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
An incremental decision list learner
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
The Proposition Bank: An Annotated Corpus of Semantic Roles
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
Question answering based on semantic structures
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Semantic role labeling of nominalized predicates in Chinese
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Semantic Role Labeling of NomBank: a maximum entropy approach
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Annotating a Japanese text corpus with predicate-argument and coreference relations
LAW '07 Proceedings of the Linguistic Annotation Workshop
Discriminative approach to predicate-argument structure analysis with zero-anaphora resolution
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
A cross-lingual ILP solution to zero anaphora resolution
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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This paper describes a new automatic method for Japanese predicate argument structure analysis. The method learns relevant features to assign case roles to the argument of the target predicate using the features of the words located closest to the target predicate under various constraints such as dependency types, words, semantic categories, parts of speech, functional words and predicate voices. We constructed decision lists in which these features were sorted by their learned weights. Using our method, we integrated the tasks of semantic role labeling and zero-pronoun identification, and achieved a 17% improvement compared with a baseline method in a sentence level performance analysis.