The electrical resistance of a graph captures its commute and cover times
STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
Confidence estimation for machine translation
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Paraphrasing for automatic evaluation
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
SenseRelate::TargetWord: a generalized framework for word sense disambiguation
ACLdemo '05 Proceedings of the ACL 2005 on Interactive poster and demonstration sessions
WordNet: similarity - measuring the relatedness of concepts
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
Graph nodes clustering based on the commute-time kernel
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Semantic classification with WordNet kernels
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Power-law distributions for paraphrases extracted from bilingual corpora
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
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Several language processing tasks can be inherently represented by a weighted graph where the weights are interpreted as a measure of relatedness between two vertices. Measuring similarity between arbitary pairs of vertices is essential in solving several language processing problems on these datasets. Random walk based measures perform better than other path based measures like shortest-path. We evaluate several random walk measures and propose a new measure based on commute time. We use the psuedo inverse of the Laplacian to derive estimates for commute times in graphs. Further, we show that this pseudo inverse based measure could be improved by discarding the least significant eigenvectors, corresponding to the noise in the graph construction process, using singular value decomposition.