Word Semantic Representations using Bayesian Probabilistic Tensor Factorization
Jingwei Zhang, Jeremy Salwen, Michael R. Glass, Alfio Gliozzo · 2014
Many forms of word relatedness have been developed, providing different perspec-tives on word similarity. We introduce a Bayesian probabilistic tensor factoriza-tion model for synthesizing a single word vector representation and per-perspective linear transformations from any number of word similarity matrices. The result-ing word vectors, when combined with the per-perspective linear transformation, ap-proximately recreate while also regulariz-ing and generalizing, each word similarity perspective. Our method can combine manually cre-ated semantic resources with neural word embeddings to separate synonyms and antonyms, and is capable of generaliz-ing to words outside the vocabulary of any particular perspective. We evaluated the word embeddings with GRE antonym questions, the result achieves the state-of-the-art performance. 1