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        <title>Analysing the Impact of Supervised Machine Learning on Automatic Term Extraction: HAMLET vs TermoStat</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Analysing the Impact of Supervised Machine Learning on Automatic Term Extraction: HAMLET vs TermoStat
%A Rigouts Terryn, Ayla
%A Drouin, Patrick
%A Hoste, Veronique
%A Lefever, Els
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F rigouts-terryn-etal-2019-analysing
%X 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.
%R 10.26615/978-954-452-056-4_117
%U https://aclanthology.org/R19-1117
%U https://doi.org/10.26615/978-954-452-056-4_117
%P 1012-1021

Markdown (Informal)

[Analysing the Impact of Supervised Machine Learning on Automatic Term Extraction: HAMLET vs TermoStat](https://aclanthology.org/R19-1117) (Rigouts Terryn et al., RANLP 2019)

• Analysing the Impact of Supervised Machine Learning on Automatic Term Extraction: HAMLET vs TermoStat (Rigouts Terryn et al., RANLP 2019)

ACL

• Ayla Rigouts Terryn, 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), pages 1012–1021, Varna, Bulgaria. INCOMA Ltd..