Article
Version 1
This version is not peer-reviewed
A Study of a Gain Based Approach for Query Aspects in Recall Oriented Tasks
Version 1
: Received: 12 September 2021 / Approved: 13 September 2021 / Online: 13 September 2021 (09:56:16 CEST)
A peer-reviewed article of this Preprint also exists.
Di Nunzio, G.M.; Faggioli, G. A Study of a Gain Based Approach for Query Aspects in Recall Oriented Tasks. Appl. Sci. 2021, 11, 9075. Di Nunzio, G.M.; Faggioli, G. A Study of a Gain Based Approach for Query Aspects in Recall Oriented Tasks. Appl. Sci. 2021, 11, 9075.
Abstract
Evidence-based healthcare integrates the best research evidence with clinical expertise in order to make decisions based on the best practices available. In this context, the task of collecting all the relevant information, a recall oriented task, in order to take the right decision within a reasonable time frame has become an important issue. In this paper, we investigate the problem of building an effective Consumer Health Search (CHS) systems that use query variations to achieve high recall and fulfill the information needs of health consumers. In particular, we study an intent-aware gain metric used to estimate the amount of missing information and make a prediction about the achievable recall for each query reformulation during a search session. We evaluate and propose alternative formulations of this metric using standard test collections of the CLEF 2018 eHealth Evaluation Lab CHS.
Keywords
Query Variations; Query Reformulations; Query Performance Prediction; Systematic Reviews
Subject
Computer Science and Mathematics, Information Systems
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment
