@inproceedings{shen2014learning,
author = {Shen, Yelong and He, Xiaodong and Gao, Jianfeng and Deng, Li and Mesnil, Gregoire},
title = {Learning Semantic Representations Using Convolutional Neural Networks for Web Search},
year = {2014},
month = {April},
abstract = {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 vector 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.},
publisher = {WWW 2014},
url = {http://approjects.co.za/?big=en-us/research/publication/learning-semantic-representations-using-convolutional-neural-networks-for-web-search/},
}