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

Autor:	Li, Menglin, Lim, Kwan Hui
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language Computer Science - Information Retrieval Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to ICLR 2024 Tiny Paper Track
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.06665 Zobrazit plný text záznamu View this record from Arxiv