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- W1487414124 abstract "indicator of semantic similarity between X and Y than the pattern X and Y. Consequently, we would like to emphasize the former pattern by assigning it a higher confidence score. It is noteworthy that all lexical patterns are not independent ‐ multiple lexical patterns can express the same semantic relation. For example, the pattern X is a large Y subsumes the more general pattern X is a Y and they both indicate a hypernymic relationship between X and Y. By clustering the semantically related patterns into groups, we can both overcome the data sparseness problem and reduce the number of parameters in training. To identify semantically related patterns, we propose a sequential pattern clustering algorithm using the distributional hypothesis [10]. We represent two words by a feature vector defined over the clusters of patterns. Finally, the semantic similarity is computed as Mahalanobis distance between points corresponding to the feature vectors. By using Mahalanobis distance instead of Euclidean distance, we can account for the inter-dependence between semantic relations. The proposed method outperforms all web-based semantic similarity measures on Miller-Charles [15] benchmark dataset." @default.
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- W1487414124 date "2009-01-01" @default.
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- W1487414124 title "A Relational Model of Semantic Similarity" @default.
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