Paper: Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank

ACL ID D13-1170
Title Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

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, this model out- performs all previous methods on several met- rics. It pushes the state of the art in single sentence positive/negative classification from 80% up to 85.4%...