Deep learning has recently shown much promise for NLP applications.
Unlike most approaches in which documents or sentences are represented
by a sparse bag-of-words vector, Stanford work at the intersection of
deep learning and natural language processing
handles variable sized sentences in a natural way and captures the
recursive nature of natural language. We explore recursive neural
networks for parsing, paraphrase detection of short phrases and longer
sentences and sentiment
analysis. Our approaches go beyong learning word vectors and instead
learn vector representations for multi-word phrases which are useful
for different applications.
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Chris Manning,
Andrew Ng and Chris Potts. 2013. Recursive Deep Models for Semantic
Compositionality Over a Sentiment Treebank. Conference on Empirical
Methods in Natural Language Processing (EMNLP 2013).
Will Zou, Richard Socher, Daniel Cer and Christopher Manning, "Bilingual Word Embeddings for Phrase-Based Machine Translation"
Richard Socher, John Bauer, Christopher D. Manning and Andrew Y. Ng, "Parsing with Compositional Vector Grammars"
Thang Luong, Richard Socher, Christopher D. Manning. 2013. Better
Word Representations with Recursive Neural Networks for Morphology.
Conference on Computational Natural Language Learning (CoNLL 2013).
Danqi Chen, Richard Socher, Christopher D. Manning, Andrew Y. Ng, "Learning New Facts From Knowledge Bases With Neural Tensor Networks and Semantic Word Vectors"
Mengqiu Wang and Christopher D. Manning, "Effect of Non-linear Deep Architecture in Sequence Labeling", ICML 2013 Workshop on Deep Learning for Audio, Speech and Language Processing
Richard Socher, Milind Ganjoo, Hamsa Sridhar, Osbert Bastani, Christopher D. Manning, Andrew Y. Ng, "Zero-Shot Learning Through Cross-Modal Transfer"
Richard Socher, Brody Huval, Christopher D. Manning and Andrew Y. Ng, "Semantic Compositionality through Recursive Matrix-Vector Spaces"
Eric H. Huang, Richard Socher, Christopher D. Manning and Andrew Y. Ng, "Improving Word Representations via Global Context and Multiple Word
Prototypes", ACL 2012
Richard Socher, Eric Huang, Jeffrey Pennington, Andrew Y. Ng, and
Christopher D. Manning. 2011. Dynamic Pooling and Unfolding
Recursive Autoencoders for Paraphrase Detection.
Advances in Neural Information Processing Systems (NIPS 2011).
Richard Socher, Jeffrey Pennington, Eric Huang, Andrew Y. Ng, and Christopher D. Manning, "Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions". EMNLP 2011.
Richard Socher, Cliff Lin, Andrew Y. Ng, and Christopher D. Manning, "Parsing Natural Scenes and Natural Language with Recursive Neural Networks". ICML 2011 (Distinguished Application Paper Award)
Richard Socher, Christopher D. Manning, Andrew Y. Ng, "Learning Continuous Phrase Representations and Syntactic Parsing with Recursive Neural Networks". NIPS Deep Learning and Unsupervised Feature Learning Workshop 2010.