Using corpus statistics and WordNet relations for sense identification
Computational Linguistics - Special issue on word sense disambiguation
Using syntactic dependency as local context to resolve word sense ambiguity
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
An algorithm for aspects of semantic interpretation using an enhanced WordNet
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Improving subcategorization acquisition using word sense disambiguation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
HLT '93 Proceedings of the workshop on Human Language Technology
Parsing the WSJ using CCG and log-linear models
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Modeling morphologically rich languages using split words and unstructured dependencies
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
The noisy channel model for unsupervised word sense disambiguation
Computational Linguistics
Generating shifting sentiment for a conversational agent
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
Unsupervised part of speech tagging using unambiguous substitutes from a statistical language model
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
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This paper describes the University of Sydney's WSD and Lexical Substitution systems for SemEval-2007. These systems are principally based on evaluating the substitutability of potential synonyms in the context of the target word. Substitutability is measured using Pointwise Mutual Information as obtained from the Web1T corpus. The WSD systems are supervised, while the Lexical Substitution system is unsupervised. The lexical sample sub-task also used syntactic category information given from a CCG-based parse to assist in verb disambiguation, while both WSD tasks also make use of more traditional features. These related systems participated in the Coarse-Grained English All-Words WSD task (task 7), the Lexical Substitution Task (task 10) and the English Lexical Sample WSD sub-task (task 17).