详解GPT-信息抽取任务 (GPT-3 FAMILY LARGE LANGUAGE MODELS)

GPT-3 FAMILY LARGE LANGUAGE MODELS

Information Extraction

自然语言处理信息提取任务（NLP-IE）：从非结构化文本数据中提取结构化数据，例如提取实体、关系和事件 164。将非结构化文本数据转换为结构化数据可以实现高效的数据处理、知识发现、决策制定并增强信息检索和搜索。

Information Extraction 子任务

信息抽取任务多种多样153：

1. 实体类型（entity typing）
2. 实体提取（entity extraction）
3. 关系分类（relation classification）
4. 关系提取（relation extraction）
5. 事件检测（event detection）
6. 事件参数提取（event argument extraction ）
7. 事件提取 （event extraction）

**Entity typing (ET)：**classifying identified named entity mentions into one of the predefined entity types 165.

**Named Entity Recognition (NER)：**identifying entity mentions and then assigning them to appropriate entity types 166.

**Relation classification (RC)：**identifying the semantic relationship between the given two target entities in a sentence 167.

**Relation Extraction (RE)：**extracting the entities and then classifying the semantic relationship between the two target entities, i.e., involves entity extraction followed by relation classification 168.

**Event Detection (ED)：**aims to identify and categorize words or phrases that trigger events 169.

**Event Argument Extraction (EAE)：**identifying event arguments, i.e., entities involved in the event and then classifying their roles 170.

**Event Extraction (EE)：**aims to extract both the events and the involved entities, i.e., it involves event detection followed by event argument extraction 171.

GPT relation classification 任务

138\], \[149\], \[153\]--\[156\], \[163

138 Y. Wang, Y. Zhao, and L. Petzold, "Are large language models ready for healthcare? a comparative study on clinical language understanding," arXiv preprint arXiv:2304.05368, 2023. chain-of-thought (CoT) self-question prompting (SQP)

链接： https://proceedings.mlr.press/v219/wang23c/wang23c.pdf

149 B. J. Gutie ́rrez, N. McNeal, C. Washington, Y. Chen, L. Li, H. Sun, and Y. Su, "Thinking about gpt-3 in-context learning for biomedical ie? think again," in Findings of the Association for Computational Linguistics: EMNLP 2022, 2022, pp. 4497--4512.

链接： https://arxiv.org/pdf/2203.08410

153 B. Li, G. Fang, Y. Yang, Q. Wang, W. Ye, W. Zhao, and S. Zhang, "Evaluating chatgpt's information extraction capabilities: An assessment of performance, explainability, calibration, and faithfulness," arXiv preprint arXiv:2304.11633, 2023.

链接： https://arxiv.org/pdf/2304.11633

154 C. Chan, J. Cheng, W. Wang, Y. Jiang, T. Fang, X. Liu, and Y. Song, "Chatgpt evaluation on sentence level relations: A focus on temporal, causal, and discourse relations," arXiv preprint arXiv:2304.14827, 2023.

链接： https://arxiv.org/pdf/2304.14827

155 X. Xu, Y. Zhu, X. Wang, and N. Zhang, "How to unleash the power of large language models for few-shot relation extraction?" arXiv preprint arXiv:2305.01555, 2023.

链接： https://arxiv.org/pdf/2305.01555

156 Z. Wan, F. Cheng, Z. Mao, Q. Liu, H. Song, J. Li, and S. Kurohashi, "Gpt-re: In-context learning for relation extraction using large language models," arXiv preprint arXiv:2305.02105, 2023. chain-of-thought (CoT)

链接： https://arxiv.org/pdf/2305.02105

163 K. Zhang, B. J. Gutie ́rrez, and Y. Su, "Aligning instruction tasks unlocks large language models as zero-shot relation extractors," arXiv preprint arXiv:2305.11159, 2023.

链接： https://arxiv.org/pdf/2305.11159

GPT relation extraction 任务

148, 151--153, 158, 161, 162,

148 X. Wei, X. Cui, N. Cheng, X. Wang, X. Zhang, S. Huang, P. Xie, J. Xu, Y. Chen, M. Zhang et al., "Zero-shot information extraction via chatting with chatgpt," arXiv preprint arXiv:2302.10205, 2023.

链接： https://eva.fing.edu.uy/pluginfile.php/524749/mod_folder/content/0/ChatIE_Zero-Shot%20Information%20Extraction%20via%20Chatting%20with%20ChatGPT.pdf

151 H. Rehana, N. B. C ̧ am, M. Basmaci, Y. He, A. ̈Ozgu ̈ r, and J. Hur, "Evaluation of gpt and bert-based models on identifying protein-protein interactions in biomedical text," arXiv preprint arXiv:2303.17728, 2023.

链接： https://pmc.ncbi.nlm.nih.gov/articles/PMC11101131/pdf/nihpp-2303.17728v2.pdf

152 C. Yuan, Q. Xie, and S. Ananiadou, "Zero-shot temporal relation extraction with chatgpt," arXiv preprint arXiv:2304.05454, 2023. chain-of-thought (CoT) event ranking (ER)

链接： https://arxiv.org/pdf/2304.05454

153 B. Li, G. Fang, Y. Yang, Q. Wang, W. Ye, W. Zhao, and S. Zhang, "Evaluating chatgpt's information extraction capabilities: An assessment of performance, explainability, calibration, and faithfulness," arXiv preprint arXiv:2304.11633, 2023.

链接： https://arxiv.org/pdf/2304.11633

158 Y. Ma, Y. Cao, Y. Hong, and A. Sun, "Large language model is not a good few-shot information extractor, but a good reranker for hard samples!" arXiv preprint arXiv:2303.08559, 2023.

链接： https://arxiv.org/pdf/2303.08559

161 S. Wadhwa, S. Amir, and B. C. Wallace, "Revisiting relation extraction in the era of large language models," arXiv preprint arXiv:2305.05003, 2023. chain-of-thought (CoT)

链接： https://pmc.ncbi.nlm.nih.gov/articles/PMC10482322/pdf/nihms-1912166.pdf

162 P. Li, T. Sun, Q. Tang, H. Yan, Y. Wu, X. Huang, and X. Qiu, "Codeie: Large code generation models are better few-shot information extractors," arXiv preprint arXiv:2305.05711, 2023.

链接： https://arxiv.org/pdf/2305.05711

Summary

参考文献

164 Y. Lu, Q. Liu, D. Dai, X. Xiao, H. Lin, X. Han, L. Sun, and H. Wu, "Unified structure generation for universal information extraction," in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022, pp. 5755--5772.

165 Y. Chen, J. Cheng, H. Jiang, L. Liu, H. Zhang, S. Shi, and R. Xu, "Learning from sibling mentions with scalable graph inference in fine-grained entity typing," in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022, pp. 2076--2087.

166 S. S. S. Das, A. Katiyar, R. J. Passonneau, and R. Zhang, "Container: Few-shot named entity recognition via contrastive learning," in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022, pp. 6338--6353.

167 S. Wu and Y. He, "Enriching pre-trained language model with entity information for relation classification," in Proceedings of the 28th ACM international conference on information and knowledge management, 2019, pp. 2361--2364.

168 D. Ye, Y. Lin, P. Li, and M. Sun, "Packed levitated marker for entity and relation extraction," in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022, pp. 4904--4917.

169 K. Zhao, X. Jin, L. Bai, J. Guo, and X. Cheng, "Knowledgeenhanced self-supervised prototypical network for few-shot event detection," in Findings of the Association for Computational Linguistics: EMNLP 2022, 2022, pp. 6266--6275.

170 Y. Ma, Z. Wang, Y. Cao, M. Li, M. Chen, K. Wang, and J. Shao, "Prompt for extraction? paie: Prompting argument interaction for event argument extraction," in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022, pp. 6759--6774.

1 A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4. 2023