Summary of Llm-der:a Named Entity Recognition Method Based on Large Language Models For Chinese Coal Chemical Domain, by Le Xiao et al.
LLM-DER:A Named Entity Recognition Method Based on Large Language Models for Chinese Coal Chemical Domain
by Le Xiao, Yunfei Xu, Jing Zhao
First submitted to arxiv on: 16 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper proposes a Large Language Models (LLMs)-based framework called LLM-DER for domain-specific Named Entity Recognition (NER) in Chinese. The goal is to recognize entities and their categories in specific domains, which is crucial for constructing domain knowledge graphs. The authors address the limitations of current few-shot methods by introducing a plausibility and consistency evaluation method to remove misrecognized entities. They demonstrate the effectiveness of LLM-DER on two datasets: Resume and Coal. Experimental results show that LLM-DER outperforms existing GPT-3.5-turbo and fully-supervised baselines, verifying its potential in domain-specific entity recognition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to recognize important words and phrases (entities) in specific areas like the coal chemical industry. They want to help build better maps of knowledge for these domains. The problem is that current methods need lots of labeled data, which can be hard to find. The authors create a new system called LLM-DER that uses big language models to recognize entities and remove mistakes. They tested it on two datasets and found that it works really well, even better than other systems. |
Keywords
» Artificial intelligence » Few shot » Gpt » Named entity recognition » Ner » Supervised
