Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions

By Richard Socher, Jeffrey Pennington, Eric Hsin-Chun Huang, Andrew Y. Ng, Christopher D. Manning. 2011

1,357 citations recorded by the source corpus.

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In Natural Language Processing

Citations in the corpus

context2vec: Learning Generic Context Embedding with Bidirectional LSTM (2016, background): “Finally, there is considerable work on using recurrent neural networks to represent word sequences, such as phrases or sentences (Socher et al., 2011; Kiros et al., 2015).”
Learning Distributed Representations of Sentences from Unlabelled Data (2016, background): “This excludes innovative supervised sentencelevel architectures including (Socher et al., 2011; Kalchbrenner et al., 2014) and many others.”
Distributed Representations of Sentences and Documents (2014, methodology): “The results also show that more advanced methods (such as Recursive Neural Network (Socher et al., 2013b)), which require parsing and take into account the compositionality, perform much better.”
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank (2013, methodology): “These vectors can be trained in an unsupervised fashion to capture distributional similarities (Collobert and Weston, 2008; Huang et al., 2012) but then also be ﬁne-tuned and trained to speciﬁc tasks such as sentiment detection (Socher et al., 2011b).”
A Neural Network for Factoid Question Answering over Paragraphs (2014, methodology): “While a softmax layer over every node in the tree could predict answers (Socher et al., 2011; Iyyer et al., 2014), this method overlooks that most answers are themselves words (features) in other questions (e.g., a question on World War II might mention the Battle of the Bulge and vice versa).”
A Convolutional Neural Network for Modelling Sentences (2014, methodology): “A model that adopts a more general structure provided by an external parse tree is the Recursive Neural Network (RecNN) (Pollack, 1990; K¨uchler and Goller, 1996; Socher et al., 2011; Hermann and Blunsom, 2013).”
Grounded Compositional Semantics for Finding and Describing Images with Sentences (2014, methodology): “RNN models (Pollack, 1990; Goller and K¨uchler, 1996; Socher et al., 2011b; Socher et al., 2011a) provided a novel way of combining word vectors for longer phrases that moved beyond simple averaging.”
Convolutional Networks on Graphs for Learning Molecular Fingerprints (2015, methodology): “A recursive neural network [23, 24] is then run from the leaves to the root to produce a fixed-size representation.”
Representation Learning: A Review and New Perspectives (2012, methodology): “Recursive auto-encoders (which generalize recurrent networks) have also been used to beat the state-of-the-art in full sentence paraphrase detection (Socher et al., 2011a) almost doubling the F1 score for paraphrase detection.”
Convolutional Neural Networks for Sentence Classification (2014, methodology): “• SST-1: Stanford Sentiment Treebank—an extension of MR but with train/dev/test splits provided and fine-grained labels (very positive, positive, neutral, negative, very negative), re-labeled by Socher et al. (2013).4”
Deep Unordered Composition Rivals Syntactic Methods for Text Classification (2015, positive): “Our recursive neural network baselines include standard RecNNs (Socher et al., 2011b), RecNTNs, the deep recursive network (DRecNN) proposed by İrsoy and Cardie (2014), and the TREE-LSTM of (Tai et al.”
Convolutional Neural Network Architectures for Matching Natural Language Sentences (2014, methodology): “Aside from the models with recursive nature [15, 21, 19] (as discussed in Section 2.”
A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines (2018, neutral): “A recursive AE [93] (Fig.”
Relational inductive biases, deep learning, and graph networks (2018, background): “pproaches with structured representations. We are excited by related approaches which have explored this idea for other types of structured representations and computations, such as linguistic trees (Socher et al., 2011a,b, 2012, 2013; Tai et al., 2015; Andreas et al., 2016), partial tree traversals”