UTD: Determining Relational Similarity Using Lexical Patterns
Bryan Rink, Sanda M. Harabagiu · 2012
In this paper we present our approach for assigning degrees of relational similarity to pairs of words in the SemEval-2012 Task 2. To measure relational similarity we employed lexical patterns that can match against word pairs within a large corpus of 12 million documents. Patterns are weighted by obtaining statistically estimated lower bounds on their precision for extracting word pairs from a given relation. Finally, word pairs are ranked based on a model predicting the probability that they belong to the relation of interest. This approach achieved the best results on the SemEval 2012 Task 2, obtaining a Spearman correlation of 0.229 and an accuracy on reproducing human answers to MaxDiff questions of 39.4%. 1