Acquiring word similarities with higher order association mining

Abstract

We present a novel approach to mine word similarity in Textual Case Based Reasoning. We exploit indirect associations of words, in addition to direct ones for estimating their similarity. If word A co-occurs with word B, we say A and B share a first order association between them. If A co-occurs with B in some documents, and B with C in some others, then A and C are said to share a second order co-occurrence via B. Higher orders of co-occurrence may similarly be defined. In this paper we present algorithms for mining higher order co-occurrences. A weighted linear model is used to combine the contribution of these higher orders into a word similarity model. Our experimental results demonstrate significant improvements compared to similarity models based on first order co-occurrences alone. Our approach also outperforms state-of-the-art techniques like SVM and LSI in classification tasks of varying complexity. © Springer-Verlag Berlin Heidelberg 2007.