Financial Product Ontology Population with Large Language Models
Chanatip Saetia, Jiratha Phruetthiset, Tawunrat Chalothorn, Monchai Lertsutthiwong, Supawat Taerungruang, Pakpoom Buabthong
Abstract
Ontology population, which aims to extract structured data to enrich domain-specific ontologies from unstructured text, typically faces challenges in terms of data scarcity and linguistic complexity, particularly in specialized fields such as retail banking. In this study, we investigate the application of large language models (LLMs) to populate domain-specific ontologies of retail banking products from Thai corporate documents. We compare traditional span-based approaches to LLMs-based generative methods, with different prompting techniques. Our findings reveal that while span-based methods struggle with data scarcity and the complex linguistic structure, LLMs-based generative approaches substantially outperform, achieving a 61.05% F1 score, with the most improvement coming from providing examples in the prompts. This improvement highlights the potential of LLMs for ontology population tasks, offering a scalable and efficient solution for structured information extraction in especially in low-resource language settings.- Anthology ID:
- 2024.textgraphs-1.4
- Volume:
- Proceedings of TextGraphs-17: Graph-based Methods for Natural Language Processing
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Dmitry Ustalov, Yanjun Gao, Alexander Panchenko, Elena Tutubalina, Irina Nikishina, Arti Ramesh, Andrey Sakhovskiy, Ricardo Usbeck, Gerald Penn, Marco Valentino
- Venues:
- TextGraphs | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 53–60
- Language:
- URL:
- https://aclanthology.org/2024.textgraphs-1.4
- DOI:
- Cite (ACL):
- Chanatip Saetia, Jiratha Phruetthiset, Tawunrat Chalothorn, Monchai Lertsutthiwong, Supawat Taerungruang, and Pakpoom Buabthong. 2024. Financial Product Ontology Population with Large Language Models. In Proceedings of TextGraphs-17: Graph-based Methods for Natural Language Processing, pages 53–60, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Financial Product Ontology Population with Large Language Models (Saetia et al., TextGraphs-WS 2024)
- PDF:
- https://preview.aclanthology.org/autopr/2024.textgraphs-1.4.pdf