Файл:Learning Semantic Representations Using Convolutional Neural Networks for Web Search Www2014 cdssm p07.pdf

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Learning_Semantic_Representations_Using_Convolutional_Neural_Networks_for_Web_Search_Www2014_cdssm_p07.pdf(0 × 0 пикселей, размер файла: 594 КБ, MIME-тип: application/pdf)

Yelong Shen Kent State University Kent, OH, USA yshen@cs.kent.e du Xiaodong He Microsoft Research Redmond, WA, USA xiaohe@microsoft. com Jianfeng Gao Microsoft Research Redmond, WA, USA jfgao@microsoft.c om Li Deng Microsoft Research Redmond, WA, USA deng@microsoft.c om Grégoire Mesnil University of Montréal Montréal, Canada gregoire.mesnil@ umontreal.ca


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.

Categories and Subject Descriptors

  • H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval;
  • I.2.6 [Artificial Intelligence]: Learning

Keywords Semantic Representation, Convolutional Neural Network

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