Learning semantic representations using convolutional neural networks for web search

Yelong Shen, Xiaodong He, Jianfeng Gao, Li Deng, Grégoire Mesnil
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
more » ... representation. The proposed models are trained on clickthrough data by maximizing the conditional likelihood of clicked documents given a query, using stochastic gradient ascent. The new models are evaluated on a Web document ranking task using a large-scale, real-world data set. Results show that our model significantly outperforms other semantic models, which were state-of-the-art in retrieval performance prior to this work.
doi:10.1145/2567948.2577348 dblp:conf/www/ShenHGDM14 fatcat:owo6nxuqnvbqlm3o6ebwp65yla