Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
An empirical study of the domain dependence of supervised word sense disambiguation systems
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Supersense tagging of unknown nouns in WordNet
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Supersense tagging of unknown nouns using semantic similarity
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Learning semantic classes for word sense disambiguation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Meaningful clustering of senses helps boost word sense disambiguation performance
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
An empirical study on class-based word sense disambiguation
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Broad-coverage sense disambiguation and information extraction with a supersense sequence tagger
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
SemEval-2010 task 17: All-words word sense disambiguation on a specific domain
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
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This paper summarizes our participation in task #17 of SemEval--2 (All--words WSD on a specific domain) using a supervised class-based Word Sense Disambiguation system. Basically, we use Support Vector Machines (SVM) as learning algorithm and a set of simple features to build three different models. Each model considers a different training corpus: SemCor (SC), examples from monosemous words extracted automatically from background data (BG), and both SC and BG (SCBG). Our system explodes the monosemous words appearing as members of a particular WordNet semantic class to automatically acquire class-based annotated examples from the domain text. We use the class-based examples gathered from the domain corpus to adapt our traditional system trained on SemCor. The evaluation reveal that the best results are achieved training with SemCor and the background examples from monosemous words, obtaining results above the first sense baseline and the fifth best position in the competition rank.