Analysing the impact of supervised machine learning on automatic term extraction : HAMLET vs TermoStat
- Author
- Ayla Rigouts Terryn, Patrick Drouin, Veronique Hoste (UGent) and Els Lefever (UGent)
- Organization
- Abstract
- Traditional approaches to automatic term extraction do not rely on machine learning (ML) and select the top n ranked candidate terms or candidate terms above a certain predefined cut-off point, based on a limited number of linguistic and statistical clues. However, supervised ML approaches are gaining interest. Relatively little is known about the impact of these supervised methodologies; evaluations are often limited to precision, and sometimes recall and f1-scores, without information about the nature of the extracted candidate terms. Therefore, the current paper presents a detailed and elaborate analysis and comparison of a traditional, state-of-the-art system (TermoStat) and a new, supervised ML approach (HAMLET), using the results obtained for the same, manually annotated, Dutch corpus about dressage.
- Keywords
- LT3
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8627335
- MLA
- Rigouts Terryn, Ayla, et al. “Analysing the Impact of Supervised Machine Learning on Automatic Term Extraction : HAMLET vs TermoStat.” Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), edited by Galia Angelova et al., INCOMA Ltd., 2019, pp. 1013–22, doi:10.26615/978-954-452-056-4_117.
- APA
- Rigouts Terryn, A., Drouin, P., Hoste, V., & Lefever, E. (2019). Analysing the impact of supervised machine learning on automatic term extraction : HAMLET vs TermoStat. In G. Angelova, R. Mitkov, I. Nikolova, & I. Temnikova (Eds.), Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019) (pp. 1013–1022). https://doi.org/10.26615/978-954-452-056-4_117
- Chicago author-date
- Rigouts Terryn, Ayla, Patrick Drouin, Veronique Hoste, and Els Lefever. 2019. “Analysing the Impact of Supervised Machine Learning on Automatic Term Extraction : HAMLET vs TermoStat.” In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), edited by Galia Angelova, Ruslan Mitkov, Ivelina Nikolova, and Irina Temnikova, 1013–22. INCOMA Ltd. https://doi.org/10.26615/978-954-452-056-4_117.
- Chicago author-date (all authors)
- Rigouts Terryn, Ayla, Patrick Drouin, Veronique Hoste, and Els Lefever. 2019. “Analysing the Impact of Supervised Machine Learning on Automatic Term Extraction : HAMLET vs TermoStat.” In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), ed by. Galia Angelova, Ruslan Mitkov, Ivelina Nikolova, and Irina Temnikova, 1013–1022. INCOMA Ltd. doi:10.26615/978-954-452-056-4_117.
- Vancouver
- 1.Rigouts Terryn A, Drouin P, Hoste V, Lefever E. Analysing the impact of supervised machine learning on automatic term extraction : HAMLET vs TermoStat. In: Angelova G, Mitkov R, Nikolova I, Temnikova I, editors. Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019). INCOMA Ltd.; 2019. p. 1013–22.
- IEEE
- [1]A. Rigouts Terryn, P. Drouin, V. Hoste, and E. Lefever, “Analysing the impact of supervised machine learning on automatic term extraction : HAMLET vs TermoStat,” in Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), Varna, Bulgaria, 2019, pp. 1013–1022.
@inproceedings{8627335,
abstract = {{Traditional approaches to automatic term extraction do not rely on machine learning (ML) and select the top n ranked candidate terms or candidate terms above a certain predefined cut-off point, based on a limited number of linguistic and statistical clues. However, supervised ML approaches are gaining interest. Relatively little is known about the impact of these supervised methodologies; evaluations are often limited to precision, and sometimes recall and f1-scores, without information about the nature of the extracted candidate terms. Therefore, the current paper presents a detailed and elaborate analysis and comparison of a traditional, state-of-the-art system (TermoStat) and a new, supervised ML approach (HAMLET), using the results obtained for the same, manually annotated, Dutch corpus about dressage.}},
author = {{Rigouts Terryn, Ayla and Drouin, Patrick and Hoste, Veronique and Lefever, Els}},
booktitle = {{Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)}},
editor = {{Angelova, Galia and Mitkov, Ruslan and Nikolova, Ivelina and Temnikova, Irina}},
isbn = {{9789544520557}},
issn = {{1313-8502}},
keywords = {{LT3}},
language = {{eng}},
location = {{Varna, Bulgaria}},
pages = {{1013--1022}},
publisher = {{INCOMA Ltd.}},
title = {{Analysing the impact of supervised machine learning on automatic term extraction : HAMLET vs TermoStat}},
url = {{http://doi.org/10.26615/978-954-452-056-4_117}},
year = {{2019}},
}
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