Contextual correlates of synonymy
Communications of the ACM
A graph model for unsupervised lexical acquisition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Novel association measures using web search with double checking
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Dependency-Based Construction of Semantic Space Models
Computational Linguistics
Learning graph walk based similarity measures for parsed text
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
WordNet::Similarity: measuring the relatedness of concepts
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
One distributional memory, many semantic spaces
GEMS '09 Proceedings of the Workshop on Geometrical Models of Natural Language Semantics
Unsupervised classification with dependency based word spaces
GEMS '09 Proceedings of the Workshop on Geometrical Models of Natural Language Semantics
Distributional memory: A general framework for corpus-based semantics
Computational Linguistics
Measuring semantic similarity between words by removing noise and redundancy in web snippets
Concurrency and Computation: Practice & Experience
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Both vector space models and graph random walk models can be used to determine similarity between concepts. Noting that vectors can be regarded as local views of a graph, we directly compare vector space models and graph random walk models on standard tasks of predicting human similarity ratings, concept categorization, and semantic priming, varying the size of the dataset from which vector space and graph are extracted.