mlsanityData to 14 Oct 2026

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

arXiv 2405.06665 ↗First posted 2 May 2024cs.CL cs.IR cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 16 of its 20 references that are in the map. First posted on arXiv 2 May 2024.

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16 of 20 references.

References · 16

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4 more
  1. 2022A Transformer-based Framework for POI-level Social Post Geolocationcited by 13
  2. 2021Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Surveycited by 12
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  1. 2025A Pluggable Multi-Task Learning Framework for Sentiment-Aware Financial Relation Extractioncited by 0