Discovering word senses from text
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Performance criteria for graph clustering and Markov cluster experiments
Performance criteria for graph clustering and Markov cluster experiments
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Measures of distributional similarity
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Discovering corpus-specific word senses
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 2
Selecting the "right" number of senses based on clustering criterion functions
EACL '06 Proceedings of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics: Posters & Demonstrations
Two graph-based algorithms for state-of-the-art WSD
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Semeval-2007 task 02: evaluating word sense induction and discrimination systems
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
UBC-AS: a graph based unsupervised system for induction and classification
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
Learning concept hierarchies from text corpora using formal concept analysis
Journal of Artificial Intelligence Research
TextGraphs-1 Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing
Graph connectivity measures for unsupervised parameter tuning of graph-based sense induction systems
UMSLLS '09 Proceedings of the Workshop on Unsupervised and Minimally Supervised Learning of Lexical Semantics
Detecting compositionality in multi-word expressions
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Taxonomy learning using word sense induction
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
UoY: Graphs of unambiguous vertices for word sense induction and disambiguation
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Expectation vectors: a semiotics inspired approach to geometric lexical-semantic representation
GEMS '10 Proceedings of the 2010 Workshop on GEometrical Models of Natural Language Semantics
Word sense induction & disambiguation using hierarchical random graphs
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Using semantic techniques to access web data
Information Systems
Word sense induction by community detection
TextGraphs-6 Proceedings of TextGraphs-6: Graph-based Methods for Natural Language Processing
Measuring the impact of sense similarity on word sense induction
EMNLP '11 Proceedings of the First Workshop on Unsupervised Learning in NLP
Creating a system for lexical substitutions from scratch using crowdsourcing
Language Resources and Evaluation
MaxMax: a graph-based soft clustering algorithm applied to word sense induction
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
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Word Sense Induction (WSI) is the task of identifying the different senses (uses) of a target word in a given text. Traditional graph-based approaches create and then cluster a graph, in which each vertex corresponds to a word that co-occurs with the target word, and edges between vertices are weighted based on the co-occurrence frequency of their associated words. In contrast, in our approach each vertex corresponds to a collocation that co-occurs with the target word, and edges between vertices are weighted based on the co-occurrence frequency of their associated collocations. A smoothing technique is applied to identify more edges between vertices and the resulting graph is then clustered. Our evaluation under the framework of SemEval-2007 WSI task shows the following: (a) our approach produces less sense-conflating clusters than those produced by traditional graph-based approaches, (b) our approach outperforms the existing state-of-the-art results.