About: Unsupervised Learning of Distributional Relation Vectors

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type	http://eprints.org/ontology/BookSectionEPrint http://eprints.org/ontology/EPrint Article Book Section
seeAlso	HTML Summary of #66991 Unsupervised Learning of Distributional Relation Vectors
http://eprints.org/ontology/hasAccepted	Unsupervised Learning of Distributional Relation Vectors (PDF)
http://eprints.org/ontology/hasDocument	Unsupervised Learning of Distributional Relation Vectors (PDF) Unsupervised Learning of Distributional Relation Vectors (Other) Unsupervised Learning of Distributional Relation Vectors (Other) Unsupervised Learning of Distributional Relation Vectors (Other) Unsupervised Learning of Distributional Relation Vectors (Other) Unsupervised Learning of Distributional Relation Vectors (Other)
dc:hasVersion	Unsupervised Learning of Distributional Relation Vectors (PDF)
Title	Unsupervised Learning of Distributional Relation Vectors
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
Date	2018-07-01
Creator	Steven Schockaert Zied Bouraoui Shoaib Jameel
status	peer reviewed published
Publisher	Association for Computational Linguistics
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 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.
Is Part Of	https://kar.kent.ac.uk/id/repository
list of authors	https://kar.kent.ac.uk/id/eprint/66991#authors
presented at	56th Annual Meeting of the Association for Computational Linguistics
volume	1
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #66991 Unsupervised Learning of Distributional Relation Vectors

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