Corpus-based stemming using cooccurrence of word variants
ACM Transactions on Information Systems (TOIS)
Two-stage language models for information retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to the special issue on word sense disambiguation: the state of the art
Computational Linguistics - Special issue on word sense disambiguation
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
NUS-ML: improving word sense disambiguation using topic features
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
SemEval-2010 task: Japanese WSD
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Word Sense Disambiguation by Combining Labeled Data Expansion and Semi-Supervised Learning Method
ACM Transactions on Asian Language Information Processing (TALIP)
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Systems using text windows to model word contexts have mostly been using fixed-sized windows and uniform weights. The window size is often selected by trial and error to maximize task results. We propose a non-supervised method for selecting weights for each window distance, effectively removing the need to limit window sizes, by maximizing the mutual generation of two sets of samples of the same word. Experiments on Semeval Word Sense Disambiguation tasks showed considerable improvements.