Paper: Word Semantic Representations using Bayesian Probabilistic Tensor Factorization

ACL ID D14-1161
Title Word Semantic Representations using Bayesian Probabilistic Tensor Factorization
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

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 resul...