Comparative Analysis of Community Detection and Transformer-Based Approaches for Topic Clustering of Scientific Papers
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F23%3A00131468" target="_blank" >RIV/00216224:14310/23:00131468 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-36805-9_42" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-36805-9_42</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-36805-9_42" target="_blank" >10.1007/978-3-031-36805-9_42</a>
Alternative languages
Result language
angličtina
Original language name
Comparative Analysis of Community Detection and Transformer-Based Approaches for Topic Clustering of Scientific Papers
Original language description
We are solving the topic clustering problem, where we need to categorize papers with initially available subjects into more consistent and higher-level topics. We approach the task from two perspectives, one is the traditional network science, where we perform community detection on a subject network with the use of Combo algorithm, and the second is the transformer-based top2vec algorithm which uses sentence-transformer to embed the content of the papers. The comparison between the two approaches was conducted using a dataset of scientific papers on computer science and mathematics collected from the SCOPUS database, and different coherence scores were used as a measure of performance. The results showed that the community detection Combo algorithm was able to achieve a similar coherence score to the transformer-based top2vec. The findings suggest that community detection may be a viable alternative for topic clustering when one has predefined topics, especially when a high coherence score and fast processing time are desired. The paper also discusses the potential advantages and limitations of using Combo for topic clustering and the potential for future work in this area.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF16_019%2F0000822" target="_blank" >EF16_019/0000822: CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
23rd International Conference on Computational Science and Its Applications , ICCSA 2023
ISBN
9783031368042
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
13
Pages from-to
648-660
Publisher name
Springer
Place of publication
Cham
Event location
Athens
Event date
Jul 3, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001166618800042