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Learning high-quality domain word embeddings is important for achieving good performance in many NLP tasks.
Is learning the n-th thing any easier than learning the first?
Sebastian Thrun · 1996
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Three new graphical models for statistical language modelling
Andriy Mnih and Geoffrey Hinton · 2007
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2010
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Word representations: a simple and general method for semi-supervised learning
Joseph Turian, Lev Ratinov, and Yoshua Bengio · 2010
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Learning word vectors for sentiment analysis
Andrew L Maas, Raymond E Daly, Peter T Pham, Dan Huang, Andrew Y Ng, and Christopher Potts · 2011
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Learning to learn
Sebastian Thrun and Lorien Pratt · 2012
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Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Linguistic regularities in continuous space word representations
Tomas Mikolov, Wen-tau Yih, and Geoffrey Zweig · 2013
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Lifelong Machine Learning Systems: Beyond Learning Algorithms
Daniel L Silver, Qiang Yang, and Lianghao Li · 2013
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Topic modeling using topics from many domains, lifelong learning and big data
Zhiyuan Chen and Bing Liu · 2014
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning · 2014
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Unsupervised cross-domain word representation learning
Danushka Bollegala, Takanori Maehara, and Ken-ichi Kawarabayashi · 2015
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Neural crf parsing
Greg Durrett and Dan Klein · 2015
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Never-Ending Learning
T Mitchell, W Cohen, E Hruschka, P Talukdar, J Betteridge, A Carlson, B Dalvi, M Gardner, B Kisiel, J Krishnamurthy, N Lao, K Mazaitis, T Mohamed, N Nakashole, E Platanios, A Ritter, M Samadi, B Settles, R Wang, D Wijaya, A Gupta, X Chen, A Saparov, M Greaves, and J Welling · 2015
Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering
R. He and J. McAuley · 2016
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Evaluating word embeddings using a representative suite of practical tasks
Neha Nayak, Gabor Angeli, and Christopher D Manning · 2016
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Lifelong-rl: Lifelong relaxation labeling for separating entities and aspects in opinion targets
Lei Shu, Bing Liu, Hu Xu, and Annice Kim · 2016
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Think globally, embed locally—locally linear meta-embedding of words
Danushka Bollegala, Kohei Hayashi, and Ken-ichi Kawarabayashi · 2017
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Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, and Nando de Freitas · 2016
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Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov · 2016
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Lifelong Machine Learning
Zhiyuan Chen and Bing Liu · 2016
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Product function need recognition via semi-supervised attention network
Hu Xu, Sihong Xie, Lei Shu, and Philip S. Yu · 2017
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A simple regularization-based algorithm for learning cross-domain word embeddings
Wei Yang, Wei Lu, and Vincent Zheng · 2017
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Double embeddings and cnn-based sequence labeling for aspect extraction
Hu Xu, Bing Liu, Lei Shu, and Philip S. Yu · 2018
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Dual attention network for product compatibility and function satisfiability analysis
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