Learning Word Embeddings for Low-Resource Languages by PU Learning

Chao Jiang, Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang


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.
Anthology ID:
N18-1093
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1024–1034
Language:
URL:
https://aclanthology.org/N18-1093
DOI:
10.18653/v1/N18-1093
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/N18-1093.pdf
Note:
 N18-1093.Notes.pdf
Video:
 http://vimeo.com/277670013