Summary of Enhancing Language Models For Financial Relation Extraction with Named Entities and Part-of-speech, by Menglin Li and Kwan Hui Lim
Enhancing Language Models for Financial Relation Extraction with Named Entities and Part-of-Speech
by Menglin Li, Kwan Hui Lim
First submitted to arxiv on: 2 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to improving the performance of pre-trained language models on the Financial Relation Extraction (FinRE) task is proposed. The strategy involves augmenting these models with Named Entity Recognition (NER) and Part-Of-Speech (POS) techniques, as well as different methods for combining this information. Experimental results on a financial relations dataset demonstrate promising outcomes and highlight the benefits of incorporating NER and POS in existing models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The FinRE task involves identifying entities and their relationships within financial statements or texts. To achieve this goal, researchers propose a simple yet effective method that enhances pre-trained language models by adding named entity recognition (NER) and part-of-speech (POS) capabilities. This approach combines these features in various ways to improve performance on a specific dataset. |
Keywords
» Artificial intelligence » Named entity recognition » Ner
