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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/P18-1003.pdf