Paper: Low-Dimensional Manifold Distributional Semantic Models

ACL ID C14-1069
Title Low-Dimensional Manifold Distributional Semantic Models
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014

Motivated by evidence in psycholinguistics and cognition, we propose a hierarchical distributed semantic model (DSM) that consists of low-dimensional manifolds built on semantic neighbor- hoods. Each semantic neighborhood is sparsely encoded and mapped into a low-dimensional space. Global operations are decomposed into local operations in multiple sub-spaces; results from these local operations are fused to come up with semantic relatedness estimates. Manifold DSM are constructed starting from a pairwise word-level semantic similarity matrix. The pro- posed model is evaluated on semantic similarity estimation task significantly improving on the state-of-the-art.