Summary of Recent Advances in Named Entity Recognition: a Comprehensive Survey and Comparative Study, by Imed Keraghel and Stanislas Morbieu and Mohamed Nadif
Recent Advances in Named Entity Recognition: A Comprehensive Survey and Comparative Study
by Imed Keraghel, Stanislas Morbieu, Mohamed Nadif
First submitted to arxiv on: 19 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 The survey presents an overview of recent advancements in Named Entity Recognition (NER) approaches, including Transformer-based methods and Large Language Models (LLMs). The paper discusses reinforcement learning and graph-based approaches that enhance NER performance. It also focuses on methods designed for datasets with scarce annotations. A comprehensive evaluation is conducted to compare the main NER implementations on various datasets with differing characteristics. The results provide insights into how dataset characteristics affect the behavior of different NER algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Named Entity Recognition tries to find words in text that refer to real things, like people or places. This survey looks at new ideas in this field, including ways to use Transformer models and Large Language Models to improve performance. It also talks about using reinforcement learning and graph-based approaches to make NER better. The paper highlights methods designed for datasets with limited training data. By comparing how different NER algorithms work on different types of datasets, the survey helps us understand how dataset characteristics affect results. |
Keywords
* Artificial intelligence * Named entity recognition * Ner * Reinforcement learning * Transformer
