About: Word Embedding as Maximum A Posteriori Estimation

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint http://eprints.org/ontology/EPrint Article Article Academic Article Academic Article
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Title	Word Embedding as Maximum A Posteriori Estimation Word Embedding as Maximum A Posteriori Estimation
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	2019-07-17 2019-07-17
Creator	Bei Shi Bei Shi Zihao Fu Zihao Fu Wai Lam Wai Lam Steven Schockart Steven Schockart Shoaib Jameel Shoaib Jameel
status	peer reviewed peer reviewed published published
Publisher	Association for the Advancement of Artificial Intelligence Association for the Advancement of Artificial Intelligence
abstract	The GloVe word embedding model relies on solving a global optimization problem, which can be reformulated as a maximum likelihood estimation problem. In this paper, we propose to generalize this approach to word embedding by considering parametrized variants of the GloVe model and incorporating priors on these parameters. To demonstrate the usefulness of this approach, we consider a word embedding model in which each context word is associated with a corresponding variance, intuitively encoding how informative it is. Using our framework, we can then learn these variances together with the resulting word vectors in a unified way. We experimentally show that the resulting word embedding models outperform GloVe, as well as many popular alternatives. The GloVe word embedding model relies on solving a global optimization problem, which can be reformulated as a maximum likelihood estimation problem. In this paper, we propose to generalize this approach to word embedding by considering parametrized variants of the GloVe model and incorporating priors on these parameters. To demonstrate the usefulness of this approach, we consider a word embedding model in which each context word is associated with a corresponding variance, intuitively encoding how informative it is. Using our framework, we can then learn these variances together with the resulting word vectors in a unified way. We experimentally show that the resulting word embedding models outperform GloVe, as well as many popular alternatives.
Is Part Of	Proceedings of the AAAI Conference on Artificial Intelligence Proceedings of the AAAI Conference on Artificial Intelligence https://kar.kent.ac.uk/id/repository https://kar.kent.ac.uk/id/repository
Subject	QA 76 Software, computer programming, QA 76 Software, computer programming,
list of authors	https://kar.kent.ac.uk/id/eprint/70009#authors https://kar.kent.ac.uk/id/eprint/70009#authors
presented at	33rd AAAI Conference on Artificial Intelligence (AAAI 2019) 33rd AAAI Conference on Artificial Intelligence (AAAI 2019)
issue	1 1
volume	33 33
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 #70009 Word Embedding as Maximum A Posteriori Estimation HTML Summary of #70009 Word Embedding as Maximum A Posteriori Estimation

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