2012 SemanticCompositionalitythrough

• (Socher et al., 2012) ⇒ Richard Socher, Brody Huval, Christopher D. Manning, and Andrew Y. Ng. (2012). “Semantic Compositionality through Recursive Matrix-Vector Spaces.” In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL 2012).

Subject Headings: Semantic Compositionality Principle; Semantic Compositionality Task

Cited By

• http://scholar.google.com/scholar?q=%222012%22+Semantic+Compositionality+through+Recursive+Matrix-Vector+Spaces

Quotes

Abstract

Single-word vector space models have been very successful at learning lexical information. However, they cannot capture the compositional meaning of longer phrases, preventing them from a deeper understanding of language. We introduce a recursive neural network (RNN) model that learns compositional vector representations for phrases and sentences of arbitrary syntactic type and length. Our model assigns a vector and a matrix to every node in a parse tree: the vector captures the inherent meaning of the constituent, while the matrix captures how it changes the meaning of neighboring words or phrases. This matrix-vector RNN can learn the meaning of operators in propositional logic and natural language. The model obtains state of the art performance on three different experiments: predicting fine-grained sentiment distributions of adverb-adjective pairs; classifying sentiment labels of movie reviews and classifying semantic relationships such as cause-effect or topic-message between nouns using the syntactic path between them.

BibTeX

@inproceedings{2012_SemanticCompositionalitythrough,
  author    = {Richard Socher and
               Brody Huval and
               Christopher D. Manning and
               Andrew Y. Ng},
  editor    = {Jun'ichi Tsujii and
               James Henderson and
               Marius Pasca},
  title     = {Semantic Compositionality through Recursive Matrix-Vector Spaces},
  booktitle = {Proceedings of the 2012 Joint Conference on Empirical Methods in Natural
               Language Processing and Computational Natural Language Learning (EMNLP-CoNLL
               2012)},
  pages     = {1201--1211},
  publisher = {{ACL}},
  year      = {2012},
  url       = {https://www.aclweb.org/anthology/D12-1110/},
}

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 SemanticCompositionalitythrough	Christopher D. Manning Andrew Y. Ng Richard Socher Brody Huval			Semantic Compositionality through Recursive Matrix-Vector Spaces						2012