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Recursive Deep Models for Semantic Compositionality Over a ...

Recursive Deep Models for Semantic CompositionalityOver a Sentiment TreebankRichard Socher, Alex Perelygin, Jean Y. Wu, Jason Chuang,Christopher D. Manning, Andrew Y. Ng and Christopher PottsStanford University, Stanford, CA 94305, word spaces have been very use-ful but cannot express the meaning of longerphrases in a principled way. Further progresstowards understanding Compositionality intasks such as sentiment detection requiresricher supervised training and evaluation re-sources and more powerful Models of remedy this, we introduce aSentiment Treebank. It includes fine grainedsentiment labels for 215,154 phrases in theparse trees of 11,855 sentences and presentsnew challenges for sentiment address them, we introduce theRecursive Neural Tensor on the new treebank, this model out-performs all previous methods on several met-rics.

fine-tuned and trained to specific tasks such as sen-timent detection (Socher et al., 2011b). The models in this paper can use purely supervised word repre-sentations learned entirely on the new corpus. Compositionality in Vector Spaces. Most of the compositionality algorithms and related datasets capture two word compositions. Mitchell and La-

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