A fully-lexicalized probabilistic model for Japanese zero anaphora resolution
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Semantic Role Labeling of NomBank: a maximum entropy approach
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
A Japanese predicate argument structure analysis using decision lists
EMNLP '08 Proceedings of the 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
Beyond NomBank: a study of implicit arguments for nominal predicates
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
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
Casting implicit role linking as an anaphora resolution task
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
Semantic role labeling of implicit arguments for nominal predicates
Computational Linguistics
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This paper presents a predicate-argument structure analysis that simultaneously conducts zero-anaphora resolution. By adding noun phrases as candidate arguments that are not only in the sentence of the target predicate but also outside of the sentence, our analyzer identifies arguments regardless of whether they appear in the sentence or not. Because we adopt discriminative models based on maximum entropy for argument identification, we can easily add new features. We add language model scores as well as contextual features. We also use contextual information to restrict candidate arguments.