About: Word and Document Embedding with vMF-Mixture Priors on Context Word Vectors

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #74583 Word and Document Embedding with vMF-Mixture Priors on Context Word Vectors
http://eprints.org/ontology/hasAccepted	Word and Document Embedding with vMF-Mixture Priors on Context Word Vectors (PDF)
http://eprints.org/ontology/hasDocument	Word and Document Embedding with vMF-Mixture Priors on Context Word Vectors (PDF) Word and Document Embedding with vMF-Mixture Priors on Context Word Vectors (Other) Word and Document Embedding with vMF-Mixture Priors on Context Word Vectors (Other) Word and Document Embedding with vMF-Mixture Priors on Context Word Vectors (Other) Word and Document Embedding with vMF-Mixture Priors on Context Word Vectors (Other) Word and Document Embedding with vMF-Mixture Priors on Context Word Vectors (Other)
dc:hasVersion	Word and Document Embedding with vMF-Mixture Priors on Context Word Vectors (PDF)
Title	Word and Document Embedding with vMF-Mixture Priors on Context Word 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 https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2019
Creator	Shoaib Jameel Steven Schockaert
status	peer reviewed published
Publisher	ACL
abstract	Word embedding models typically learn two types of vectors: target word vectors and context word vectors. These vectors are normally learned such that they are predictive of some word co-occurrence statistic, but they are otherwise unconstrained. However, the words from a given language can be organized in various natural groupings, such as syntactic word classes (e.g. nouns, adjectives, verbs) and semantic themes (e.g. sports, politics, sentiment). Our hypothesis in this paper is that embedding models can be improved by explicitly imposing a cluster structure on the set of context word vectors. To this end, our model relies on the assumption that context word vectors are drawn from a mixture of von Mises- Fisher (vMF) distributions, where the parameters of this mixture distribution are jointly optimized with the word vectors. We show that this results in word vectors which are qualitatively different from those obtained with existing word embedding models. We furthermore show that our embedding model can also be used to learn high-quality document representations.
Is Part Of	https://kar.kent.ac.uk/id/repository
list of authors	https://kar.kent.ac.uk/id/eprint/74583#authors
presented at	The 57th Annual Meeting of the Association for Computational Linguistics (ACL)
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #74583 Word and Document Embedding with vMF-Mixture Priors on Context Word Vectors

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