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Learning high-quality embeddings for rare words is a hard problem because of sparse context information.
Mining and summarizing customer reviews
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Software Framework for Topic Modelling with Large Corpora
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The westbury lab wikipedia corpus
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Evaluating word embeddings in multi-label classification using fine-grained name typing
Yadollah Yaghoobzadeh, Katharina Kann, and Hinrich Schütze. 2018 · 2010
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Efficient estimation of word representations in vector space
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Crowdsourcing a word–emotion association lexicon
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Adam: A method for stochastic optimization
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Not all contexts are created equal: Better word representations with variable attention
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John Wieting, Mohit Bansal, Kevin Gimpel, and Karen Livescu. 2016 · 2016
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Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Morph-fitting: Fine-tuning word vector spaces with simple language-specific rules
Ivan Vulić, Nikola Mrkšić, Roi Reichart, Diarmuid Ó Séaghdha, Steve Young, and Anna Korhonen. 2017 · 2017
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Generalizing and improving bilingual word embedding mappings with a multi-step framework of linear transformations
Mikel Artetxe, Gorka Labaka, and Eneko Agirre. 2018 · 2018
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A la carte embedding: Cheap but effective induction of semantic feature vectors
Mikhail Khodak, Nikunj Saunshi, Yingyu Liang, Tengyu Ma, Brandon Stewart, and Sanjeev Arora. 2018 · 2018
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Incorporating subword information into matrix factorization word embeddings
Alexandre Salle and Aline Villavicencio. 2018 · 2018
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Learning semantic representations for novel words: Leveraging both form and context
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