Abstract
Large Language Models (LLMs) have brought us efficient tools for various natural language processing (NLP) tasks. This paper explores the application of LLMs for extracting domain-specific terms from textual data. We will present the advantages and limitations of using LLMs for this task and will highlight the significant improvements they offer over traditional terminology extraction methods such as rule-based and statistical approaches.- Anthology ID:
- 2023.nlp4tia-1.9
- Volume:
- Proceedings of the First Workshop on NLP Tools and Resources for Translation and Interpreting Applications
- Month:
- September
- Year:
- 2023
- Address:
- Varna, Bulgaria
- Editors:
- Raquel Lázaro Gutiérrez, Antonio Pareja, Ruslan Mitkov
- Venues:
- NLP4TIA | WS
- SIG:
- Publisher:
- INCOMA Ltd., Shoumen, Bulgaria
- Note:
- Pages:
- 57–60
- Language:
- URL:
- https://aclanthology.org/2023.nlp4tia-1.9
- DOI:
- Cite (ACL):
- Julie Giguere. 2023. Leveraging Large Language Models to Extract Terminology. In Proceedings of the First Workshop on NLP Tools and Resources for Translation and Interpreting Applications, pages 57–60, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.
- Cite (Informal):
- Leveraging Large Language Models to Extract Terminology (Giguere, NLP4TIA-WS 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.nlp4tia-1.9.pdf