Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
A cluster algorithm for graphs
A cluster algorithm for graphs
Two graph-based algorithms for state-of-the-art WSD
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Semeval-2007 task 02: evaluating word sense induction and discrimination systems
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
The SemEval-2007 WePS evaluation: establishing a benchmark for the web people search task
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Evaluating and optimizing the parameters of an unsupervised graph-based WSD algorithm
TextGraphs-1 Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing
Web People Search with Domain Ranking
TSD '08 Proceedings of the 11th international conference on Text, Speech and Dialogue
Word Sense Induction Using Graphs of Collocations
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
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
Conceptual knowledge acquisition using automatically generated large-scale semantic networks
ICCS'10 Proceedings of the 18th international conference on Conceptual structures: from information to intelligence
Evaluating unsupervised ensembles when applied to word sense induction
ACL '12 Proceedings of ACL 2012 Student Research Workshop
Evaluating Word Sense Induction and Disambiguation Methods
Language Resources and Evaluation
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This paper describes a graph-based unsupervised system for induction and classification. The system performs a two stage graph based clustering where a cooccurrence graph is first clustered to compute similarities against contexts. The context similarity matrix is pruned and the resulting associated graph is clustered again by means of a random-walk type algorithm. The system relies on a set of parameters that have been tuned to fit the corpus data. The system has participated in tasks 2 and 13 of the SemEval-2007 competition, on word sense induction and Web people search, respectively, with mixed results.