Supervised Distributional Semantic Relatedness
Distributional measures of semantic relatedness determine word similarity based on how frequently a pair of words appear in the same contexts. A typical method is to construct a word-context matrix, then re-weight it using some measure of association, and finally take the vector distance as a measure of similarity. This has largely been an unsupervised process, but in recent years more work has been done devising methods of using known sets of synonyms to enhance relatedness measures. This paper examines and expands on one such measure, which learns a weighting of a word-context matrix by measuring associations between words appearing in a given context and sets of known synonyms. In doing so we propose a general method of learning weights for word-context matrices, and evaluate it on a word similarity task. This method works with a variety of measures of association and can be trained with synonyms from any resource.
KeywordsTraining Data Semantic Relatedness Computational Linguistics Context Level Pointwise Mutual Information
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