Evaluating Automatic Term Extraction Methods on Individual Documents

Antonio Šajatović, Maja Buljan, Jan Šnajder, Bojana Dalbelo Bašić


Abstract
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 KeyConceptRelatendess surpass others in document-level ATE.
Anthology ID:
W19-5118
Volume:
Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)
Month:
August
Year:
2019
Address:
Florence, Italy
Venues:
ACL | MWE | WS
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
149–154
Language:
URL:
https://www.aclweb.org/anthology/W19-5118
DOI:
10.18653/v1/W19-5118
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W19-5118.pdf