2016/07/13 by James Henderson, Henderson, James, Diana Nicoleta Popa +1
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #cs.CL #cs.LG
paper · pdf · doi:10.48550/arxiv.1607.03780
To appear in Proc. 54th Annual Meeting of the Association Computational Linguistics (ACL 2016)
arxiv created 2016/07/13 · arxiv updated 2016/07/14
Distributional semantics creates vector-space representations that capture many forms of semantic similarity, but their relation to semantic entailment has been less clear. We propose a vector-space model which provides a formal foundation for a distributional semantics of entailment. Using a mean-field approximation, we develop approximate inference procedures and entailment operators over vectors of probabilities of features being known (versus unknown). We use this framework to reinterpret an existing distributional-semantic model (Word2Vec) as approximating an entailment-based model of the distributions of words in contexts, thereby predicting lexical entailment relations. In both unsupervised and semi-supervised experiments on hyponymy detection, we get substantial improvements over previous results.