Short Text Hashing Improved by Integrating Multi-granularity Topics and Tags

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• 作者：Jiaming Xu (14)
  Bo Xu (14)
  Guanhua Tian (14)
  Jun Zhao (14)
  Fangyuan Wang (14)
  Hongwei Hao (14)
  
  14. Institute of Automation ; Chinese Academy of Sciences ; 100190 ; Beijing ; P.R. China
  
• 关键词：Similarity Search ; Hashing ; Topic Features ; Short Text
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9041
• 期：1
• 页码：444-455
• 全文大小：358 KB
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• 作者单位：Computational Linguistics and Intelligent Text Processing
• 丛书名：978-3-319-18110-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Due to computational and storage efficiencies of compact binary codes, hashing has been widely used for large-scale similarity search. Unfortunately, many existing hashing methods based on observed keyword features are not effective for short texts due to the sparseness and shortness. Recently, some researchers try to utilize latent topics of certain granularity to preserve semantic similarity in hash codes beyond keyword matching. However, topics of certain granularity are not adequate to represent the intrinsic semantic information. In this paper, we present a novel unified approach for short text Hashing using Multi-granularity Topics and Tags, dubbed HMTT. In particular, we propose a selection method to choose the optimal multi-granularity topics depending on the type of dataset, and design two distinct hashing strategies to incorporate multi-granularity topics. We also propose a simple and effective method to exploit tags to enhance the similarity of related texts. We carry out extensive experiments on one short text dataset as well as on one normal text dataset. The results demonstrate that our approach is effective and significantly outperforms baselines on several evaluation metrics.