Enhancing Language Models for Financial Relation Extraction with Named Entities and Part-of-Speech
This work addresses relation extraction in financial texts, which is an incremental improvement for domain-specific applications.
The paper tackles the Financial Relation Extraction (FinRE) problem by augmenting pre-trained language models with Named Entity Recognition (NER) and Part-of-Speech (POS) information, showing promising results on a financial dataset.
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.