Learning semantic representations using convolutional neural networks for web search

@inproceedings{Shen2014LearningSR,
  title={Learning semantic representations using convolutional neural networks for web search},
  author={Yelong Shen and Xiaodong He and Jianfeng Gao and Li Deng and Gr{\'e}goire Mesnil},
  booktitle={WWW},
  year={2014}
}
This paper presents a series of new latent semantic models based on a convolutional neural network (CNN) to learn low-dimensional semantic vectors for search queries and Web documents. By using the convolution-max pooling operation, local contextual information at the word n-gram level is modeled first. Then, salient local fea-tures in a word sequence are combined to form a global feature vector. Finally, the high-level semantic information of the word sequence is extracted to form a global… CONTINUE READING
Highly Influential
This paper has highly influenced 27 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 249 citations. REVIEW CITATIONS
158 Citations
0 References
Similar Papers

Citations

Publications citing this paper.

249 Citations

05010020142015201620172018
Citations per Year
Semantic Scholar estimates that this publication has 249 citations based on the available data.

See our FAQ for additional information.

Similar Papers

Loading similar papers…