Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank Richard Socher, Alex Perelygin, Jean Y. Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng and Christopher Potts Stanford University, Stanford, CA 94305, USA richard@socher.org,{aperelyg,jcchuang,ang}@cs.stanford.edu {jeaneis,manning,cgpotts}@stanford.edu Abstract Semantic word spaces have been very use- ful but cannot express the meaning of longer phrases in a principled way. Further progress towards understanding compositionality in tasks such as sentiment detection requires richer supervised training and evaluation re- sources and more powerful models of com- position. To remedy this, we introduce a Sentiment Treebank. It includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment composition- ality. To address them, we introduce the Recursive Neural Tensor Network. When trained on the new treebank, th
