Unsupervised Learning of Distributional Relation Vectors

Shoaib Jameel, Zied Bouraoui, Steven Schockaert


Abstract
Word embedding models such as GloVe rely on co-occurrence statistics to learn vector representations of word meaning. While we may similarly expect that co-occurrence statistics can be used to capture rich information about the relationships between different words, existing approaches for modeling such relationships are based on manipulating pre-trained word vectors. In this paper, we introduce a novel method which directly learns relation vectors from co-occurrence statistics. To this end, we first introduce a variant of GloVe, in which there is an explicit connection between word vectors and PMI weighted co-occurrence vectors. We then show how relation vectors can be naturally embedded into the resulting vector space.
Anthology ID:
P18-1003
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Iryna Gurevych, Yusuke Miyao
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
23–33
Language:
URL:
https://aclanthology.org/P18-1003
DOI:
10.18653/v1/P18-1003
Bibkey:
Cite (ACL):
Shoaib Jameel, Zied Bouraoui, and Steven Schockaert. 2018. Unsupervised Learning of Distributional Relation Vectors. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 23–33, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Unsupervised Learning of Distributional Relation Vectors (Jameel et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-1003.pdf
Video:
 https://aclanthology.org/P18-1003.mp4