A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2015; you can also visit the original URL.
The file type is application/pdf
.
Learning semantic representations using convolutional neural networks for web search
2014
Proceedings of the 23rd International Conference on World Wide Web - WWW '14 Companion
This paper presents a series of new latent semantic models based on a convolutional neural network (CNN) to learn lowdimensional 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 features 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
doi:10.1145/2567948.2577348
dblp:conf/www/ShenHGDM14
fatcat:owo6nxuqnvbqlm3o6ebwp65yla