Paper Info
Title
Convolutional neural networks for text hashing
Abstract
Hashing, as a popular approximate nearest neighbor search, has been widely used for large-scale similarity search. Recently, a spectrum of machine learning methods are utilized to learn similarity-preserving binary codes. However, most of them directly encode the explicit features, keywords, which fail to preserve the accurate semantic similarities in binary code beyond keyword matching, especially on short texts. Here we propose a novel text hashing framework with convolutional neural networks. In particular, we first embed the keyword features into compact binary code with a locality preserving constraint. Meanwhile word features and position features are together fed into a convolutional network to learn the implicit features which are further incorporated with the explicit features to fit the pretrained binary code. Such base method can be successfully accomplished without any external tags/labels, and other three model variations are designed to integrate tags/labels. Experimental results show the superiority of our proposed approach over several state-of-the-art hashing methods when tested on one short text dataset as well as one normal text dataset.
Year
Venue
Field
2015
IJCAI
ENCODE,Locality,Pattern recognition,Computer science,Convolutional neural network,Binary code,Hash function,Artificial intelligence,Nearest neighbor search
DocType
Citations 
PageRank 
Conference
7
0.45
References 
Authors
17
7
Name
Order
Citations
PageRank
Jiaming Xu128435.34
Peng Wang21439.89
Guanhua Tian31126.31
Bo Xu424136.59
Jun Zhao52119115.52
Fangyuan Wang6462.43
Hong-Wei Hao71636.29