Enhancing Language Models for Financial Relation Extraction with Named Entities and Part-of-Speech

Menglin Li, Kwan Hui Lim

Keywords: Relation Extraction, Transformers, Language Models, Finance, Natural Language Processing

TL;DR: A simple but effective strategy to improve the performance of pre-trained language models via augmentation with Named Entity Recognition and Part Of Speech tagging.

Abstract: The Financial Relation Extraction (FinRE) task involves identifying the entities and their relation, given a piece of financial statement/text. To solve this FinRE problem, we propose a simple but effective strategy that improves the performance of pre-trained language models by augmenting them with Named Entity Recognition (NER) and Part-Of-Speech (POS), as well as different approaches to combine these information. Experiments on a financial relations dataset show promising results and highlights the benefits of incorporating NER and POS in existing models. Our dataset and codes are available at https://github.com/kwanhui/FinRelExtract.

Submission Number: 91