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        <title>Tempo-Lexical Context Driven Word Embedding for Cross-Session Search Task Extraction</title>
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    <abstract>Task extraction is the process of identifying search intents over a set of queries potentially spanning multiple search sessions. Most existing research on 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. Embedded query vectors are then clustered into tasks. Experiments demonstrate that task extraction effectiveness is improved significantly with our proposed method of query vector embedding in comparison to existing approaches that make use of documents retrieved from a collection to estimate semantic similarities between queries.</abstract>
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%0 Conference Proceedings
%T Tempo-Lexical Context Driven Word Embedding for Cross-Session Search Task Extraction
%A Sen, Procheta
%A Ganguly, Debasis
%A Jones, Gareth
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F sen-etal-2018-tempo
%X Task extraction is the process of identifying search intents over a set of queries potentially spanning multiple search sessions. Most existing research on 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. Embedded query vectors are then clustered into tasks. Experiments demonstrate that task extraction effectiveness is improved significantly with our proposed method of query vector embedding in comparison to existing approaches that make use of documents retrieved from a collection to estimate semantic similarities between queries.
%R 10.18653/v1/N18-1026
%U https://aclanthology.org/N18-1026/
%U https://doi.org/10.18653/v1/N18-1026
%P 283-292

Markdown (Informal)

[Tempo-Lexical Context Driven Word Embedding for Cross-Session Search Task Extraction](https://aclanthology.org/N18-1026/) (Sen et al., NAACL 2018)

• Tempo-Lexical Context Driven Word Embedding for Cross-Session Search Task Extraction (Sen et al., NAACL 2018)

ACL

• Procheta Sen, Debasis Ganguly, and Gareth Jones. 2018. Tempo-Lexical Context Driven Word Embedding for Cross-Session Search Task Extraction. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 283–292, New Orleans, Louisiana. Association for Computational Linguistics.