Comparative Analysis of Community Detection and Transformer-Based Approaches for Topic Clustering ...
BRETSKO, Daniel, Aliaksandr BELY a Stanislav SOBOLEVSKY. Comparative Analysis of Community Detection and Transformer-Based Approaches for Topic Clustering of Scientific Papers. In Osvaldo Gervasi, Beniamino Murgante, David Taniar, Bernady O. Apduhan, Ana Cristina Braga, Chiara Garau, Anastasia Stratigea. 23rd International Conference on Computational Science and Its Applications , ICCSA 2023. Cham: Springer, 2023, s. 648-660. ISBN 978-3-031-36804-2. Dostupné z: https://dx.doi.org/10.1007/978-3-031-36805-9_42. |
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Základní údaje | |
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Originální název | Comparative Analysis of Community Detection and Transformer-Based Approaches for Topic Clustering of Scientific Papers |
Autoři | BRETSKO, Daniel (804 Ukrajina, garant, domácí), Aliaksandr BELY (112 Bělorusko, domácí) a Stanislav SOBOLEVSKY (112 Bělorusko, domácí). |
Vydání | Cham, 23rd International Conference on Computational Science and Its Applications , ICCSA 2023, od s. 648-660, 13 s. 2023. |
Nakladatel | Springer |
Další údaje | |
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Originální jazyk | angličtina |
Typ výsledku | Stať ve sborníku |
Obor | 10201 Computer sciences, information science, bioinformatics |
Stát vydavatele | Německo |
Utajení | není předmětem státního či obchodního tajemství |
Forma vydání | tištěná verze "print" |
WWW | URL |
Impakt faktor | Impact factor: 0.402 v roce 2005 |
Kód RIV | RIV/00216224:14310/23:00131468 |
Organizační jednotka | Přírodovědecká fakulta |
ISBN | 978-3-031-36804-2 |
ISSN | 0302-9743 |
Doi | http://dx.doi.org/10.1007/978-3-031-36805-9_42 |
UT WoS | 001166618800042 |
Klíčová slova anglicky | Network analysis; NLP; Topic clustering; Community detection; Sentence-transformers |
Štítky | rivok |
Příznaky | Mezinárodní význam, Recenzováno |
Změnil | Změnila: Mgr. Marie Šípková, DiS., učo 437722. Změněno: 21. 3. 2024 10:32. |
Anotace |
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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. |
Návaznosti | |
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EF16_019/0000822, projekt VaV | Název: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur |
MUNI/J/0008/2021, interní kód MU | Název: Digital City |
Investor: Masarykova univerzita, Digital City, MASH JUNIOR - MUNI Award In Science and Humanities JUNIOR |
VytisknoutZobrazeno: 2. 8. 2024 06:47