Bayesian Hierarchical Words Representation Learning

Oren Barkan; Idan Rejwan; Avi Caciularu; Noam Koenigstein

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arXiv · 2004.07126

Bayesian Hierarchical Words Representation Learning

Oren Barkan·Idan Rejwan·Avi Caciularu·Noam Koenigstein

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Abstract

This paper presents the Bayesian Hierarchical Words Representation (BHWR) learning algorithm. BHWR facilitates Variational Bayes word representation learning combined with semantic taxonomy modeling via hierarchical priors. By propagating relevant information between related words, BHWR utilizes the taxonomy to improve the quality of such representations. Evaluation of several linguistic datasets demonstrates the advantages of BHWR over suitable alternatives that facilitate Bayesian modeling with or without semantic priors. Finally, we further show that BHWR produces better representations for rare words.

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Oren Barkan, Idan Rejwan, Avi Caciularu, Noam Koenigstein. 2020-04-12. Bayesian Hierarchical Words Representation Learning. https://arxiv.org/abs/2004.07126

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