2014 ConvolutionalNeuralNetworkArchi

• (Hu et al., 2014) ⇒ Baotian Hu, Zhengdong Lu, Hang Li, and Qingcai Chen. (2014). “Convolutional Neural Network Architectures for Matching Natural Language Sentences.” In: Proceedings of the 27th International Conference on Neural Information Processing Systems (NIPS'14).

Subject Headings: Convolutional Neural Network; Natural Language Processing

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Convolutional+Neural+Network+Architectures+for+Matching+Natural+Language+Sentences
• http://dl.acm.org/citation.cfm?id=2969033.2969055&preflayout=flat#citedby
• http://w.hangli-hl.com/uploads/3/1/6/8/3168008/hu-etal-nips2014.pdf
• http://papers.nips.cc/paper/5550-convolutional-neural-network-architectures-for-matching-natural-language-sentences

Quotes

Abstract

Semantic matching is of central importance to many natural language tasks [2, 28 ]. A successful matching algorithm needs to adequately model the internal structures of language objects and the interaction between them. As a step toward this goal, we propose convolutional neural network models for matching two sentences, by adapting the convolutional strategy in vision and speech. The proposed models not only nicely represent the hierarchical structures of sentences with their layer-by-layer composition and pooling, but also capture the rich matching patterns at different levels. Our models are rather generic, requiring no prior knowledge on language, and can hence be applied to matching tasks of different nature and in different languages. The empirical study on a variety of matching tasks demonstrates the efficacy of the proposed model on a variety of matching tasks and its superiority to competitor models.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 ConvolutionalNeuralNetworkArchi	Hang Li Zhengdong Lu Baotian Hu Qingcai Chen			Convolutional Neural Network Architectures for Matching Natural Language Sentences