Learning Word Embeddings for Low-resource Languages by PU Learning

Chao Jiang, Hsiang-Fu Yu, Cho-Jui Hsieh, and Kai-Wei Chang, in NAACL, 2018.

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Word embedding has been used as 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 very sparse because many word pairs are not observed to co-occur. In contrast to existing approaches, we argue that the zero entries in the co-occurrence matrix also provide valuable information and design a Positive-Unlabeled Learning (PU-Learning) approach to factorize the co-occurrence matrix. The experimental results demonstrate that the proposed approach requires a smaller amount of training text to obtain a reasonable word embedding model.

Bib Entry

  author = {Jiang, Chao and Yu, Hsiang-Fu and Hsieh, Cho-Jui and Chang, Kai-Wei},
  title = {Learning Word Embeddings for Low-resource Languages by PU Learning},
  booktitle = {NAACL},
  year = {2018}