Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Evaluating sense disambiguation across diverse parameter spaces
Natural Language Engineering
Parameter optimization for machine-learning of word sense disambiguation
Natural Language Engineering
The Penn Chinese TreeBank: Phrase structure annotation of a large corpus
Natural Language Engineering
Simple features for Chinese word sense disambiguation
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Investigations into the role of lexical semantics in word sense disambiguation
Investigations into the role of lexical semantics in word sense disambiguation
Annotating the propositions in the Penn Chinese Treebank
SIGHAN '03 Proceedings of the second SIGHAN workshop on Chinese language processing - Volume 17
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
Chinese verb sense discrimination using an EM clustering model with rich linguistic features
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
The role of semantic roles in disambiguating verb senses
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Automatic semantic role labeling for Chinese verbs
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
English tasks: all-words and verb lexical sample
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
A New Decision Rule for Statistical Word Sense Disambiguation
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
A mixture model with sharing for lexical semantics
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Applying collocation segmentation to the ACL anthology reference corpus
ACL '12 Proceedings of the ACL-2012 Special Workshop on Rediscovering 50 Years of Discoveries
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In this paper we present word sense disambiguation (WSD) experiments on ten highly polysemous verbs in Chinese, where significant performance improvements are achieved using rich linguistic features. Our system performs significantly better, and in some cases substantially better, than the baseline on all ten verbs. Our results also demonstrate that features extracted from the output of an automatic Chinese semantic role labeling system in general benefited the WSD system, even though the amount of improvement was not consistent across the verbs. For a few verbs, semantic role information actually hurt WSD performance. The inconsistency of feature performance is a general characteristic of the WSD task, as has been observed by others. We argue that this result can be explained by the fact that word senses are partitioned along different dimensions for different verbs and the features therefore need to be tailored to particular verbs in order to achieve adequate accuracy on verb sense disambiguation.