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Tempo-lexical context driven word embedding for cross-session search task extraction

Sen, Procheta, Ganguly, Debasis orcid logoORCID: 0000-0003-0050-7138 and Jones, Gareth J.F. orcid logoORCID: 0000-0003-2923-8365 (2018) Tempo-lexical context driven word embedding for cross-session search task extraction. In: 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1-6 June 2018, New Orleans, LA, USA.

Abstract
Search task extraction in information retrieval is the process of identifying search intents over a set of queries relating to the same topical information need. Search tasks may potentially span across multiple search sessions. Most existing research on search task extraction has focused on identifying tasks within a single session, where the notion of a session is defined by a fixed length time window. By contrast, in this work we seek to identify tasks that span across multiple sessions. To identify tasks, we conduct a global analysis of a query log in its entirety without restricting analysis to individual temporal windows. To capture inherent task semantics, we represent queries as vectors in an abstract space. We learn the embedding of query words in this space by leveraging the temporal and lexical contexts of queries. To evaluate the effectiveness of the proposed query embedding, we conduct experiments of clustering queries into tasks with a particular interest of measuring the cross-session search task recall. Results of our experiments demonstrate that task extraction effectiveness, including cross-session recall, is improved significantly with the help of our proposed method of embedding the query terms by leveraging the temporal and templexical contexts of queries.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Information retrieval
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: Proceedings of NAACL-HLT 2018. 1. Association for Computational Linguistics (ACL).
Publisher:Association for Computational Linguistics (ACL)
Official URL:http://dx.doi.org/10.18653/v1/N18-1026
Copyright Information:© 2018 Association for Computational Linguistics (ACL)
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland as part of the ADAPT Centre (Grant No. 13/RC/2106)
ID Code:23401
Deposited On:05 Jun 2019 13:53 by Thomas Murtagh . Last Modified 05 Jun 2019 13:53
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