Transcription of Recursive Deep Models for Semantic Compositionality Over a ...
{{id}} {{{paragraph}}}
Example: quiz answers
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.
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,faperelyg,jcchuang,angg@cs.stanford.edu fjeaneis,manning,cgpottsg@stanford.edu Abstract Semantic word spaces have been very use …
Loading..
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: