@inproceedings{jameel-etal-2018-unsupervised,
title = "Unsupervised Learning of Distributional Relation Vectors",
author = "Jameel, Shoaib and
Bouraoui, Zied and
Schockaert, Steven",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1003",
doi = "10.18653/v1/P18-1003",
pages = "23--33",
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.",
}
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%0 Conference Proceedings
%T Unsupervised Learning of Distributional Relation Vectors
%A Jameel, Shoaib
%A Bouraoui, Zied
%A Schockaert, Steven
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F jameel-etal-2018-unsupervised
%X 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.
%R 10.18653/v1/P18-1003
%U https://aclanthology.org/P18-1003
%U https://doi.org/10.18653/v1/P18-1003
%P 23-33
Markdown (Informal)
[Unsupervised Learning of Distributional Relation Vectors](https://aclanthology.org/P18-1003) (Jameel et al., ACL 2018)
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.