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A query-driven topic model

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Fang, Zheng, He, Yulan and Procter, Rob (2021) A query-driven topic model. In: The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021), Bangkok, Thailand, 1-6 Aug 2021. Published in: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 pp. 1764-1777. doi:10.18653/v1/2021.findings-acl.154

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Official URL: https://doi.org/10.18653/v1/2021.findings-acl.154

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Abstract

Topic modeling is an unsupervised method for revealing the hidden semantic structure of a corpus. It has been increasingly widely adopted as a tool in the social sciences, including political science, digital humanities and sociological research in general. One desirable property of topic models is to allow users to find topics describing a specific aspect of the corpus. A possible solution is to incorporate domain-specific knowledge into topic modeling, but this requires a specification from domain experts. We propose a novel query-driven topic model that allows users to specify a simple query in words or phrases and return query-related topics, thus avoiding tedious work from domain experts. Our proposed approach is particularly attractive when the user-specified query has a low occurrence in a text corpus, making it difficult for traditional topic models built on word cooccurrence patterns to identify relevant topics. Experimental results demonstrate the effectiveness of our model in comparison with both classical topic models and neural topic models.

Item Type: Conference Item (Paper)
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH): Information retrieval, Computational linguistics, Corpora (Linguistics) -- Data processing, Data mining, Machine learning, Artificial intelligence, Semantic Web
Journal or Publication Title: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
Publisher: Association for Computational Linguistics
Official Date: August 2021
Dates:
DateEvent
August 2021Available
6 May 2021Accepted
Page Range: pp. 1764-1777
DOI: 10.18653/v1/2021.findings-acl.154
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Copyright Holders: ©2021 Association for Computational Linguistics
Date of first compliant deposit: 3 June 2021
Date of first compliant Open Access: 4 June 2021
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
EP/T017112/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
EP/V048597/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
EP/V020579/1UK Research and Innovationhttp://dx.doi.org/10.13039/100014013
EP/N510129/1UK Research and Innovationhttp://dx.doi.org/10.13039/100014013
UNSPECIFIEDChina Scholarship Councilhttp://dx.doi.org/10.13039/501100004543
UNSPECIFIEDUniversity of Warwickhttp://dx.doi.org/10.13039/501100000741
Conference Paper Type: Paper
Title of Event: The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021)
Type of Event: Conference
Location of Event: Bangkok, Thailand
Date(s) of Event: 1-6 Aug 2021
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