Summary of A Survey on Cutting-edge Relation Extraction Techniques Based on Language Models, by Jose A. Diaz-garcia and Julio Amador Diaz Lopez
A survey on cutting-edge relation extraction techniques based on language models
by Jose A. Diaz-Garcia, Julio Amador Diaz Lopez
First submitted to arxiv on: 27 Nov 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 comprehensive survey delves into the latest advancements in Relation Extraction (RE), a pivotal task in natural language processing essential for applications across biomedical, financial, and legal sectors. The study highlights the evolution and current state of RE techniques by analyzing 137 papers presented at the Association for Computational Linguistics (ACL) conferences over the past four years, focusing on models that leverage language models like BERT-based methods. Our findings underscore the dominance of these methods in achieving state-of-the-art results for RE while also noting the promising capabilities of emerging large language models (LLMs) like T5, especially in few-shot relation extraction scenarios where they excel in identifying previously unseen relations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Relation Extraction is a crucial task in natural language processing that can be applied across various sectors. A recent study looked at the latest advancements in RE by analyzing 137 papers presented at ACL conferences over four years. The study found that BERT-based methods are leading the way in achieving state-of-the-art results for RE, but also noted the potential of new large language models like T5. |
Keywords
» Artificial intelligence » Bert » Few shot » Natural language processing » T5
