Improving Document Ranking for Long Queries with Nested Query Segmentation

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• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9626
• 期：1
• 页码：775-781
• 全文大小：246 KB
• 参考文献：1.Li, Y., Hsu, B.J.P., Zhai, C., Wang, K.: Unsupervised query segmentation using clickthrough for information retrieval. In: SIGIR 2011, pp. 285–294 (2011)
  2.Saha Roy, R., Ganguly, N., Choudhury, M., Laxman, S.: An IR-based evaluation framework for web search query segmentation. In: SIGIR 2012, pp. 881–890 (2012)
  3.Tan, B., Peng, F.: Unsupervised query segmentation using generative language models and Wikipedia. In: WWW 2008, pp. 347–356 (2008)
  4.Mishra, N., Saha Roy, R., Ganguly, N., Laxman, S., Choudhury, M.: Unsupervised query segmentation using only query logs. In: WWW 2011, pp. 91–92 (2011)
  5.Hagen, M., Potthast, M., Stein, B., Bräutigam, C.: Query segmentation revisited. In: WWW 2011, pp. 97–106 (2011)
  6.Cummins, R., O’Riordan, C.: Learning in a pairwise term-term proximity framework for information retrieval. In: SIGIR 2009, pp. 251–258 (2009)
  7.Chaudhari, D.L., Damani, O.P., Laxman, S.: Lexical co-occurrence, statistical significance, and word association. In: EMNLP 2011, pp. 1058–1068 (2011)
  8.Agichtein, E., Brill, E., Dumais, S.: Improving web search ranking by incorporating user behavior information. In: SIGIR 2006, pp. 19–26 (2006)
  9.Huang, J., Gao, J., Miao, J., Li, X., Wang, K., Behr, F., Giles, C.L.: Exploring web scale language models for search query processing. In: WWW 2010, pp. 451–460 (2010)
  
• 作者单位：Rishiraj Saha Roy (21)
  Anusha Suresh (22)
  Niloy Ganguly (22)
  Monojit Choudhury (23)
  
  21. Max Planck Institute for Informatics, Saarbrücken, Germany
  22. Indian Institute of Technology (IIT), Kharagpur, India
  23. Microsoft Research India, Bangalore, India
  
• 丛书名：Advances in Information Retrieval
• ISBN：978-3-319-30671-1
• 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

In this research, we explore nested or hierarchical query segmentation (An extended version of this paper is available at http://​rese arch.​microsoft.​com/​pubs/​259980/​2015-msri-tr-nest-seg.​pdf), where segments are defined recursively as consisting of contiguous sequences of segments or query words, as a more effective representation of a query. We design a lightweight and unsupervised nested segmentation scheme, and propose how to use the tree arising out of the nested representation of a query to improve ranking performance. We show that nested segmentation can lead to significant gains over state-of-the-art flat segmentation strategies.