Multi-Relational Latent Semantic Analysis

We present Multi-Relational Latent Semantic Analysis (MRLSA) which generalizes Latent Semantic Analysis (LSA).MRLSA provides an elegant approach to combining multiple relations between words by constructing a 3-way tensor.Similar to LSA, a lowrank approximation of the tensor is derived using a tensor decomposition.Each word in the vocabulary is thus represented by a vector in the latent semantic space and each relation is captured by a latent square matrix.The degree of two words having a specific relation can then be measured through simple linear algebraic operations.We demonstrate that by integrating multiple relations from both homogeneous and heterogeneous information sources, MRLSA achieves stateof-the-art performance on existing benchmark datasets for two relations, antonymy and is-a.