Topical Word Embeddings
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Zhiyuan Liu | Yang Liu | Maosong Sun | Tat-Seng Chua | Zhiyuan Liu | Maosong Sun | Tat-Seng Chua | Y. Liu
[1] Yoshua Bengio,et al. A Neural Probabilistic Language Model , 2003, J. Mach. Learn. Res..
[2] Andrew Y. Ng,et al. Improving Word Representations via Global Context and Multiple Word Prototypes , 2012, ACL.
[3] Christopher D. Manning,et al. Better Word Representations with Recursive Neural Networks for Morphology , 2013, CoNLL.
[4] Max Welling,et al. Fast collapsed gibbs sampling for latent dirichlet allocation , 2008, KDD.
[5] Yoshua Bengio,et al. Word Representations: A Simple and General Method for Semi-Supervised Learning , 2010, ACL.
[6] Mark Steyvers,et al. Finding scientific topics , 2004, Proceedings of the National Academy of Sciences of the United States of America.
[7] George A. Miller,et al. WordNet: A Lexical Database for English , 1995, HLT.
[8] Geoffrey E. Hinton,et al. A Scalable Hierarchical Distributed Language Model , 2008, NIPS.
[9] Hinrich Schütze,et al. Introduction to information retrieval , 2008 .
[10] Quoc V. Le,et al. Distributed Representations of Sentences and Documents , 2014, ICML.
[11] Michael I. Jordan,et al. Hierarchical Dirichlet Processes , 2006 .
[12] Georgiana Dinu,et al. Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors , 2014, ACL.
[13] Yoshua Bengio,et al. Neural Probabilistic Language Models , 2006 .
[14] Andrew McCallum,et al. Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space , 2014, EMNLP.
[15] Jason Weston,et al. A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.
[16] Wanxiang Che,et al. Learning Sense-specific Word Embeddings By Exploiting Bilingual Resources , 2014, COLING.
[17] Enhong Chen,et al. A Probabilistic Model for Learning Multi-Prototype Word Embeddings , 2014, COLING.
[18] Chih-Jen Lin,et al. LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..
[19] Alexander J. Smola,et al. Scalable inference in latent variable models , 2012, WSDM '12.
[20] Jason Weston,et al. Natural Language Processing (Almost) from Scratch , 2011, J. Mach. Learn. Res..
[21] Eric P. Xing,et al. MedLDA: maximum margin supervised topic models for regression and classification , 2009, ICML '09.
[22] Andrew Y. Ng,et al. Parsing with Compositional Vector Grammars , 2013, ACL.
[23] Jeffrey Dean,et al. Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.
[24] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[25] Michael I. Jordan,et al. DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification , 2008, NIPS.
[26] Andrew Y. Ng,et al. Parsing Natural Scenes and Natural Language with Recursive Neural Networks , 2011, ICML.
[27] Alexander J. Smola,et al. An architecture for parallel topic models , 2010, Proc. VLDB Endow..
[28] Raymond J. Mooney,et al. Multi-Prototype Vector-Space Models of Word Meaning , 2010, NAACL.
[29] David M. Blei,et al. Supervised Topic Models , 2007, NIPS.
[30] Ehud Rivlin,et al. Placing search in context: the concept revisited , 2002, TOIS.
[31] Michael I. Jordan,et al. Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..