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Word embedding
Results: 101

#	Item
21	Fully Unsupervised Small-Vocabulary Speech Recognition Using a Segmental Bayesian Model Herman Kamper1,2 , Aren Jansen3 , Sharon Goldwater2 1 CSTR and 2 ILCC, School of Informatics, University of Edinburgh, UK Add to Reading List Source URL: www.cstr.inf.ed.ac.uk Language: English - Date: 2015-09-29 11:06:25 Statistics Machine learning Artificial intelligence Statistical models Markov models Language modeling Computational linguistics Mixture model Word embedding Image segmentation Hidden Markov model Speech recognition
22	Hierarchical Memory Networks Sarath Chandar∗ 1 , Sungjin Ahn1 , Hugo Larochelle2,4 , Pascal Vincent1,4 , Gerald Tesauro3 , Yoshua Bengio1,4 arXiv:1605.07427v1 [stat.ML] 24 May 2016 Add to Reading List Source URL: arxiv.org Language: English - Date: 2016-05-24 20:30:33 Computational neuroscience Artificial neural networks Cognitive science Applied mathematics Neuroscience Machine learning Advanced RISC Computing MIPS Technologies Deep learning MIPS instruction set Softmax function Word embedding
23	Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning Yulia Tsvetkov♠ Manaal Faruqui♠ Wang Ling♣ Brian MacWhinney♠ Chris Dyer♣♠ ♠ Carnegie Mellon University ♣ G Add to Reading List Source URL: www.aclweb.org Language: English - Date: 2016-08-01 10:37:36 Machine learning Computational linguistics Artificial neural networks Natural language processing Graphical models Word embedding Word-sense disambiguation Word2vec Conditional random field Named-entity recognition Curriculum Support vector machine
24	Learning Natural Language Inference with LSTM Shuohang Wang School of Information Systems Singapore Management University Add to Reading List Source URL: arxiv.org Language: English - Date: 2015-12-30 20:26:50 Artificial neural networks Long short-term memory Artificial intelligence Recurrent neural network Word embedding Word2vec Automatic summarization Textual entailment Deep learning
25	Do Multi-Sense Embeddings Improve Natural Language Understanding? Dan Jurafsky Computer Science Department Stanford University Stanford, CA 94305, USA Add to Reading List Source URL: nlp.stanford.edu Language: English - Date: 2015-10-31 15:44:36 Computational linguistics Semantics Natural language processing Lexical semantics Word-sense disambiguation Word embedding Word2vec Language model Semantic similarity SemEval Embedding Artificial neural network
26	arXiv:1506.06726v1 [cs.CL] 22 JunSkip-Thought Vectors Ryan Kiros 1 , Yukun Zhu 1 , Ruslan Salakhutdinov 1,2 , Richard S. Zemel 1,2 Antonio Torralba 3 , Raquel Urtasun 1 , Sanja Fidler 1 Add to Reading List Source URL: arxiv.org Language: English - Date: 2015-06-22 20:32:14 Artificial neural networks Computational linguistics Machine learning Computational neuroscience Semantics Deep learning Semantic similarity Word embedding Word2vec Feature learning Language model Recurrent neural network
27	The Vector Space Model of Word Meaning Informatics 1 CG: Lecture 13 Reading: An Introduction to Latent Semantic Analysis. Discourse Processes, 25, 259–284. Add to Reading List Source URL: www.inf.ed.ac.uk Language: English - Date: 2016-02-08 08:36:36 Natural language processing Computational linguistics Semantics Vectors Machine learning Latent semantic analysis Semantic similarity Distributional semantics Word embedding Vector space model Document-term matrix Euclidean vector
28	Globally Normalized Transition-Based Neural Networks Daniel Andor, Chris Alberti, David Weiss, Aliaksei Severyn, Alessandro Presta, Kuzman Ganchev, Slav Petrov and Michael Collins∗ Google Inc New York, NY {andor,chrisa Add to Reading List Source URL: arxiv.org Language: English - Date: 2016-06-08 20:54:40 Artificial neural networks Language modeling Computational neuroscience Mathematical psychology N-gram Word embedding Conditional random field Perceptron
29	EgoSet: Exploiting Word Ego-networks and User-generated Ontology for Multifaceted Set Expansion Xin Rong1 , Zhe Chen2 , Qiaozhu Mei1,2 , Eytan Adar1,2 1 School of Information, 2 Computer Science and Engineering Add to Reading List Source URL: www.cond.org Language: English - Date: 2015-12-14 21:19:03 Computational linguistics Natural language processing Artificial neural networks Language modeling Data mining Word2vec Cluster analysis Word embedding N-gram Word-sense disambiguation Hierarchical clustering Ontology
30	I present a new similarity score, based on a statistical model, that is useful for clustering problems with high missing data rates and discrete data values. In settings that range from genomics to recommender systems, I Add to Reading List Source URL: mmds-data.org Language: English - Date: 2016-06-23 15:50:48 Recommender systems Data mining Collaborative filtering Collaborative software Collective intelligence Social information processing Cluster analysis SimRank Word embedding

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