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Unsupervised Learning of Distributional Relation Vectors

Jameel, Shoaib and Bouraoui, Zied and Schockaert, Steven (2018) Unsupervised Learning of Distributional Relation Vectors. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics. ISBN 978-1-948087-32-2.

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Word embedding models such as GloVe rely on co-occurrence statistics to learn vector representations of word meaning. While we may similarly expect that cooccurrence 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.

Item Type: Book section
Divisions: Faculties > Sciences > School of Computing > Data Science
Depositing User: Shoaib Jameel
Date Deposited: 11 May 2018 08:53 UTC
Last Modified: 11 Oct 2019 15:25 UTC
Resource URI: (The current URI for this page, for reference purposes)
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