WordNet: a lexical database for English
Communications of the ACM
SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Unsupervised Graph-basedWord Sense Disambiguation Using Measures of Word Semantic Similarity
ICSC '07 Proceedings of the International Conference on Semantic Computing
Word sense disambiguation: A survey
ACM Computing Surveys (CSUR)
SemEval-2007 task 17: English lexical sample, SRL and all words
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
English tasks: all-words and verb lexical sample
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Combining orthogonal monolingual and multilingual sources of evidence for all words WSD
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
COLEUR and COLSLM: A WSD approach to multilingual lexical substitution, tasks 2 and 3 SemEval 2010
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
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Word Sense Disambiguation remains one of the most complex problems facing computational linguists to date. In this paper we present modification to the graph based state of the art algorithm In-Degree. Our modifications entail augmenting the basic Lesk similarity measure with more relations based on the structure of WordNet, adding SemCor examples to the basic WordNet lexical resource and finally instead of using the LCH similarity measure for computing verb verb similarity in the In-Degree algorithm, we use JCN. We report results on three standard data sets using three different versions of WordNet. We report the highest performing monolingual unsupervised results to date on the Senseval 2 all words data set. Our system yields a performance of 62.7% using WordNet 1.7.1.