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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 (In Press)
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WRAP-query-driven-topic-model-Fang-2021.pdf - Accepted Version - Requires a PDF viewer. Download (992Kb) | Preview |
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) | |||||||||||||||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | |||||||||||||||||||||
| Divisions: | Faculty of 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 | |||||||||||||||||||||
| Official Date: | 2021 | |||||||||||||||||||||
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| Status: | Peer Reviewed | |||||||||||||||||||||
| Publication Status: | In Press | |||||||||||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||||||||
| RIOXX Funder/Project Grant: |
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| 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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