Parameter optimization for machine-learning of word sense disambiguation
Natural Language Engineering
Integrating multiple knowledge sources to disambiguate word sense: an exemplar-based approach
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Dutch word sense disambiguation: optimizing the localness of context
WSD '02 Proceedings of the ACL-02 workshop on Word sense disambiguation: recent successes and future directions - Volume 8
SemEval-2010 task 2: Cross-lingual lexical substitution
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
SemEval-2010 task 3: Cross-lingual word sense disambiguation
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
ParaSense or how to use parallel corpora for word sense disambiguation
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Multilingual WSD with just a few lines of code: the BabelNet API
ACL '12 Proceedings of the ACL 2012 System Demonstrations
Joining forces pays off: multilingual joint word sense disambiguation
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Five languages are better than one: an attempt to bypass the data acquisition bottleneck for WSD
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
The cross-lingual lexical substitution task
Language Resources and Evaluation
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This paper describes the Cross-Lingual Word Sense Disambiguation system UvT-WSD1, developed at Tilburg University, for participation in two SemEval-2 tasks: the Cross-Lingual Word Sense Disambiguation task and the Cross-Lingual Lexical Substitution task. The UvT-WSD1 system makes use of k-nearest neighbour classifiers, in the form of single-word experts for each target word to be disambiguated. These classifiers can be constructed using a variety of local and global context features, and these are mapped onto the translations, i.e. the senses, of the words. The system works for a given language-pair, either English-Dutch or English-Spanish in the current implementation, and takes a word-aligned parallel corpus as its input.