Evaluating Automatic Term Extraction Methods on Individual Documents

• Antonio Šajatović ^[1] ; Maja Buljan ^[1] ; Jan Šnajder ^[1] ; Bojana Dalbelo Bašić ^[1]
  1. [1] University of Zagreb

     University of Zagreb

     Croacia

     
• Localización: Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019): August 2, 2019 Florence, Italy: Proceedings of the Workshop / Agata Savary (ed. lit.), Carla Parra Escartín (ed. lit.), Francis Bond (ed. lit.), Jelena Mitrovic (ed. lit.), Verginica Barbu Mititelu (ed. lit.), 2019, ISBN 978-1-950737-26-0, págs. 149-154
• Idioma: inglés
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• Resumen

  • Automatic Term Extraction (ATE) extracts terminology from domain-specific corpora. ATE is used in many NLP tasks, including Computer Assisted Translation, where it is typically applied to individual documents rather than the entire corpus. While corpus-level ATE has been extensively evaluated, it is not obvious how the results transfer to document- level ATE. To fill this gap, we evaluate 16 state-of-the-art ATE methods on full-length documents from three different domains, on both corpus and document levels. Unlike existing studies, our evaluation is more realistic as we take into account all gold terms. We show that no single method is best in corpus- level ATE, but C-Value and KeyConcept Relatendess surpass others in document-level ATE.