In this paper we use Natural Language Processing techniques to improve different machine learning approaches (Support Vector Machines (SVM), Local SVM, Random Forests) to the problem of automatic keyphrases extraction from scientific papers. For the evaluation we propose a large and high-quality dataset: 2000 ACM papers from the Computer Science domain. We evaluate by comparison with expert-assigned keyphrases.

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[1] Mikalai Krapivin, "Krapivin", IEEE Dataport, 2024. [Online]. Available: http://dx.doi.org/10.21227/dsad-ed75. Accessed: Jan. 13, 2025.
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doi = {10.21227/dsad-ed75},
url = {http://dx.doi.org/10.21227/dsad-ed75},
author = {Mikalai Krapivin },
publisher = {IEEE Dataport},
title = {Krapivin},
year = {2024} }
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T1 - Krapivin
AU - Mikalai Krapivin
PY - 2024
PB - IEEE Dataport
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Mikalai Krapivin. (2024). Krapivin. IEEE Dataport. http://dx.doi.org/10.21227/dsad-ed75
Mikalai Krapivin, 2024. Krapivin. Available at: http://dx.doi.org/10.21227/dsad-ed75.
Mikalai Krapivin. (2024). "Krapivin." Web.
1. Mikalai Krapivin. Krapivin [Internet]. IEEE Dataport; 2024. Available from : http://dx.doi.org/10.21227/dsad-ed75
Mikalai Krapivin. "Krapivin." doi: 10.21227/dsad-ed75