Learning Word Embeddings for Low-resource Languages by PU Learning

ACL 2018

Learning Word Embeddings for Low-resource Languages by PU Learning

Jun 29, 2018
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Abstract: Word embedding is a key component in many downstream applications in processing natural languages. Existing approaches often assume the existence of a large collection of text for learning effective word embedding. However, such a corpus may not be available for some low-resource languages. In this paper, we study how to effectively learn a word embedding model on a corpus with only a few million tokens. In such a situation, the co-occurrence matrix is sparse as the co-occurrences of many word pairs are unobserved. In contrast to existing approaches often only sample a few unobserved word pairs as negative samples, we argue that the zero entries in the co-occurrence matrix also provide valuable information. We then design a Positive-Unlabeled Learning (PU-Learning) approach to factorize the co-occurrence matrix and validate the proposed approaches in four different languages. Authors: Chao Jiang, Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang (University of Virginia, Amazon, University of California Davis, University of California Los Angeles)

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