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Query-Task Mapping

Published: 18 July 2019 Publication History

Abstract

Several recent task-based search studies aim at splitting query logs into sets of queries for the same task or information need. We address the natural next step: mapping a currently submitted query to an appropriate task in an already task-split log. This query-task mapping can, for instance, enhance query suggestions---rendering efficiency of the mapping, besides accuracy, a key objective. Our main contributions are three large benchmark datasets and preliminary experiments with four query-task mapping approaches: (1) a Trie-based approach, (2) MinHash~LSH, (3) word movers distance in a Word2Vec setup, and (4) an inverted index-based approach. The experiments show that the fast and accurate inverted index-based method forms a strong baseline.

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Cited By

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  • (2025)Search task extraction using k-contour based recurrent deep graph clusteringEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109501139(109501)Online publication date: Jan-2025
  • (2022)SaL-Lightning Dataset: Search and Eye Gaze Behavior, Resource Interactions and Knowledge Gain during Web SearchACM SIGIR Conference on Human Information Interaction and Retrieval10.1145/3498366.3505835(347-352)Online publication date: 14-Mar-2022
  • (2021)CoST: An annotated Data Collection for Complex SearchProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3481998(4455-4464)Online publication date: 26-Oct-2021
  • Show More Cited By

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cover image ACM Conferences
SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
July 2019
1512 pages
ISBN:9781450361729
DOI:10.1145/3331184
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 18 July 2019

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Author Tags

  1. query-task mapping
  2. task-based search

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  • Short-paper

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  • Bundesministerium für Bildung und Forschung

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SIGIR '19
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SIGIR'19 Paper Acceptance Rate 84 of 426 submissions, 20%;
Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

View all
  • (2025)Search task extraction using k-contour based recurrent deep graph clusteringEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109501139(109501)Online publication date: Jan-2025
  • (2022)SaL-Lightning Dataset: Search and Eye Gaze Behavior, Resource Interactions and Knowledge Gain during Web SearchACM SIGIR Conference on Human Information Interaction and Retrieval10.1145/3498366.3505835(347-352)Online publication date: 14-Mar-2022
  • (2021)CoST: An annotated Data Collection for Complex SearchProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3481998(4455-4464)Online publication date: 26-Oct-2021
  • (2021)Modeling User Search Tasks with a Language-Agnostic Unsupervised ApproachAdvances in Information Retrieval10.1007/978-3-030-72113-8_27(405-418)Online publication date: 27-Mar-2021
  • (2021)Extracting Search Tasks from Query Logs Using a Recurrent Deep Clustering ArchitectureAdvances in Information Retrieval10.1007/978-3-030-72113-8_26(391-404)Online publication date: 27-Mar-2021

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