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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. It pushes the state of the art in singlesentence positive/negative classification from80% up to The accuracy of predictingfine-grained sentiment labels for all phrasesreaches , an improvement of overbag of features baselines.

Recursive Neural Tensor Network (RNTN). Recur-sive Neural Tensor Networks take as input phrases of any length. They represent a phrase through word vectors and a parse tree and then compute vectors for higher nodes in the tree using the same tensor-based composition function. We compare to several super-vised, compositional models such as ...

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