The Library
A query-driven topic model
Tools
Tools
Tools
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
|
PDF
WRAP-query-driven-topic-model-Fang-2021.pdf - Accepted Version - Requires a PDF viewer. Download (992Kb) | Preview |
Official URL: https://doi.org/10.18653/v1/2021.findings-acl.154
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: |
|
|||||||||||||||||||||
| 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: |
|
|||||||||||||||||||||
| 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 | |||||||||||||||||||||
| Related URLs: |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
