Papers with ACE05 datasets
GPT-RE: In-context Learning for Relation Extraction using Large Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to in-context learning (ICL) are lacking in relation extraction (RE) . emergence of large language models (LLMs) such as GPT-3 represents a significant advancement in natural language processing. |
| Approach: | They propose to incorporate task-aware representations into demonstration retrieval and enrich the demonstrations with gold label-induced reasoning logic. |
| Outcome: | The proposed model achieves SOTA and competitive performances on the Semeval and SciERC datasets. |
Dependency Parsing-Based Syntactic Enhancement of Relation Extraction in Scientific Texts (2025.findings-emnlp)
Copied to clipboard
| Challenge: | a pipeline approach to extract entities and relations from scientific text is challenging due to long sentences with densely packed entities. |
| Approach: | They propose a syntactic filtering method that prunes unlikely entity pairs before relation prediction. |
| Outcome: | The proposed method improves Rel+ F1 scores on SciERC, SciER, and ACE05 datasets. |