Learning Natural Language Inference with LSTM Shuohang Wang School of Information Systems Singapore Management University

Source URL: arxiv.org

• Artificial neural networks
• Long short-term memory
• Artificial intelligence
• Recurrent neural network
• Word embedding
• Word2vec
• Automatic summarization
• Textual entailment
• Deep learning

Do Multi-Sense Embeddings Improve Natural Language Understanding? Dan Jurafsky Computer Science Department Stanford University Stanford, CA 94305, USA

Source URL: nlp.stanford.edu

• Computational linguistics
• Semantics
• Natural language processing
• Lexical semantics
• Word-sense disambiguation
• Word embedding
• Word2vec
• Language model
• Semantic similarity
• SemEval
• Embedding
• Artificial neural network

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

Source URL: arxiv.org

• Artificial neural networks
• Computational linguistics
• Machine learning
• Computational neuroscience
• Semantics
• Deep learning
• Semantic similarity
• Word embedding
• Word2vec
• Feature learning
• Language model
• Recurrent neural network

word2vec Parameter Learning Explained Xin Rong Abstract The word2vec model and application by Mikolov et al. have attracted a great

Source URL: www-personal.umich.edu

• Artificial neural networks
• Backpropagation
• Matrix
• Word2vec
• Euclidean vector
• Tensor
• Deep learning

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

Source URL: www.cond.org

• 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

MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com Context-Sensitive and Role-Dependent Spoken Language Understanding using Bidirectional and Attention LSTMs Hori, C.; Hori, T.; Watanabe, S.; Hershey, J.R.

Source URL: www.merl.com

• Artificial neural networks
• Computational neuroscience
• Deep learning
• Recurrent neural network
• Long short-term memory
• Speech recognition
• Artificial intelligence
• Dialog manager
• Softmax function
• Word2vec
• Sepp Hochreiter

DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs Prashanth Vijayaraghavan, Ivan Sysoev, Soroush Vosoughi and Deb Roy MIT Media Lab, Massachusetts Institute of Technology C

Source URL: socialmachines.media.mit.edu

• Computational neuroscience
• Artificial neural networks
• Machine learning
• Computer vision
• Convolutional neural network
• Deep learning
• Twitter
• Convolution
• Hashtag
• Word2vec
• Tweet
• Pattern recognition

An  Ensemble  for  Query  Intent  Detec4on   rd CIKM  CUP  2014,  3  Place   Elmar  Haussmann   University  of  Freiburg,  Germany   Chair  of  Algorithm  and

Source URL: ad-publications.informatik.uni-freiburg.de

• Machine learning
• Artificial neural networks
• Word2vec
• Google Search
• Web query classification

A Model of Zero-Shot Learning of Spoken Language Understanding Majid Yazdani Computer Science Department University of Geneva

Source URL: majid.yazdani.me

• Statistical classification
• Machine learning
• Artificial neural networks
• Support vector machine
• Word embedding
• Feature learning
• Multi-label classification
• Word2vec
• Margin classifier
• Language model

word2vec Parameter Learning Explained arXiv:1411.2738v3 [cs.CL] 30 Jan 2016 Xin Rong

Source URL: arxiv.org