On metric embedding for boosting semantic similarity computations

International audienceComputing pairwise word semantic similarity is widely used and serves as a building block in many tasks in NLP. In this paper, we explore the embedding of the shortest-path metrics from a knowledge base (Wordnet) into the Hamming hyper-cube, in order to enhance the computation performance. We show that, although an isometric embedding is untractable, it is possible to achieve good non-isometric embeddings. We report a speedup of three orders of magnitude for the task of computing Leacock and Chodorow (LCH) similarity while keeping strong correlations (r = .819, ρ = .826)