Contextual correlates of synonymy
Communications of the ACM
Placing search in context: the concept revisited
ACM Transactions on Information Systems (TOIS)
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
Word association norms, mutual information, and lexicography
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
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
Measuring semantic similarity between words using web search engines
Proceedings of the 16th international conference on World Wide Web
Combining association measures for collocation extraction
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
WikiRelate! computing semantic relatedness using wikipedia
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
A study on similarity and relatedness using distributional and WordNet-based approaches
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
WikiWalk: random walks on Wikipedia for semantic relatedness
TextGraphs-4 Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing
| Hi-index | 0.00 |
Lexical co-occurrence is an important cue for detecting word associations. We propose a new measure of word association based on a new notion of statistical significance for lexical co-occurrences. Existing measures typically rely on global unigram frequencies to determine expected co-occurrence counts. Instead, we focus only on documents that contain both terms (of a candidate word-pair) and ask if the distribution of the observed spans of the word-pair resembles that under a random null model. This would imply that the words in the pair are not related strongly enough for one word to influence placement of the other. However, if the words are found to occur closer together than explainable by the null model, then we hypothesize a more direct association between the words. Through extensive empirical evaluation on most of the publicly available benchmark data sets, we show the advantages of our measure over existing co-occurrence measures.