Plongement de métrique pour le calcul de similarité sémantique à l'échelle

Plongement de metrique pour le calcul de similarite semantique a l'echelle Resume. In this paper, we explore the embedding of the shortest-path metrics from a knowledge base (Wordnet) into the Hamming hypercube, 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) similarities while keeping strong correlations.