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
- Editors:
- Agata Savary, Carla Parra Escartín, Francis Bond, Jelena Mitrović, Verginica Barbu Mititelu
- Venue:
- MWE
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 149–154
- Language:
- URL:
- https://aclanthology.org/W19-5118
- DOI:
- 10.18653/v1/W19-5118
- Cite (ACL):
- Antonio Šajatović, Maja Buljan, Jan Šnajder, and Bojana Dalbelo Bašić. 2019. Evaluating Automatic Term Extraction Methods on Individual Documents. In Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019), pages 149–154, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Evaluating Automatic Term Extraction Methods on Individual Documents (Šajatović et al., MWE 2019)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W19-5118.pdf
- Data
- GENIA