Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
A fully unsupervised word sense disambiguation method using dependency knowledge
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
UMND2: SenseClusters applied to the sense induction task of Senseval-4
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
SemEval-2010 task 14: Word sense induction & disambiguation
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Measuring the impact of sense similarity on word sense induction
EMNLP '11 Proceedings of the First Workshop on Unsupervised Learning in NLP
Evaluating Word Sense Induction and Disambiguation Methods
Language Resources and Evaluation
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Word sense induction and discrimination (WSID) identifies the senses of an ambiguous word and assigns instances of this word to one of these senses. We have build a WSID system that exploits syntactic and semantic features based on the results of a natural language parser component. To achieve high robustness and good generalization capabilities, we designed our system to work on a restricted, but grammatically rich set of features. Based on the results of the evaluations our system provides a promising performance and robustness.