Richard Socher – Using RNN for NLP

Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank

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Abstract – Semantic word spaces have been very useful
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 resources
and more powerful models of composition.
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 compositionality.
To address them, we introduce the
Recursive Neural Tensor Network. When
trained on the new treebank, this model outperforms
all previous methods on several metrics.
It pushes the state of the art in single
sentence positive/negative classification from
80% up to 85.4%. The accuracy of predicting
fine-grained sentiment labels for all phrases
reaches 80.7%, an improvement of 9.7% over
bag of features baselines. Lastly, it is the only
model that can accurately capture the effects
of negation and its scope at various tree levels
for both positive and negative phrases. https://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf