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Enhancing Literature Reviews in Human-Machine Collaboration: A Comparative Analysis of Topic Modeling Methods in Computer Science

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

This study conducts a targeted literature review on human-machine collaboration (HMC) within computer science from 2019 to 2023, utilizing topic modeling to address challenges such as outdated research and manual curation costs. We compare Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF) to determine their effectiveness in analyzing HMC literature. Our results show that NMF, which leverages TF-IDF weighting, produces more distinct and interpretable topics compared to the overlapping clusters often generated by LDA. Specifically, NMF identifies four key themes in HMC: natural language processing applications, the integration of human strengths with algorithms, advancements in image-related tasks, and the application and evaluation of AI agents. These findings suggest that NMF is better suited for capturing nuanced research trends in HMC. This study provides valuable insights for improving literature review methodologies and advancing the understanding of human-machine collaboration in computer science.

Original languageEnglish
Title of host publicationTechnologies and Applications of Artificial Intelligence - 29th International Conference, TAAI 2024, Proceedings
EditorsWei-Ta Chu, Chih-Ya Shen, Hong-Han Shuai
PublisherSpringer Science and Business Media Deutschland GmbH
Pages235-246
Number of pages12
ISBN (Print)9789819645886
DOIs
StatePublished - 2025
Event29th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2024 - Hsinchu, Taiwan
Duration: 6 Dec 20247 Dec 2024

Publication series

NameCommunications in Computer and Information Science
Volume2414 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference29th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2024
Country/TerritoryTaiwan
CityHsinchu
Period6/12/247/12/24

Keywords

  • human-machine collaboration (HMC)
  • literature review
  • topic modeling

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