Information Extraction
Information extraction (IE) focuses on automatically extracting structured information from unstructured or semi-structured text, aiming to convert raw data into usable formats for various applications. Current research emphasizes unified IE frameworks, often leveraging large language models (LLMs) and incorporating retrieval-augmented generation (RAG) techniques, along with advancements in neural architectures like LSTMs and transformers. This field is crucial for knowledge base construction, enabling efficient data analysis across diverse domains like healthcare, finance, and humanitarian aid, and improving the accuracy and speed of data processing in numerous applications.
Papers
Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset
Chongjian Yue, Xinrun Xu, Xiaojun Ma, Lun Du, Zhiming Ding, Shi Han, Dongmei Zhang, Qi Zhang
OneKE: A Dockerized Schema-Guided LLM Agent-based Knowledge Extraction System
Yujie Luo, Xiangyuan Ru, Kangwei Liu, Lin Yuan, Mengshu Sun, Ningyu Zhang, Lei Liang, Zhiqiang Zhang, Jun Zhou, Lanning Wei, Da Zheng, Haofen Wang, Huajun Chen
Increasing the Accessibility of Causal Domain Knowledge via Causal Information Extraction Methods: A Case Study in the Semiconductor Manufacturing Industry
Houssam Razouk, Leonie Benischke, Daniel Garber, Roman Kern
Information Extraction from Clinical Notes: Are We Ready to Switch to Large Language Models?
Yan Hu, Xu Zuo, Yujia Zhou, Xueqing Peng, Jimin Huang, Vipina K. Keloth, Vincent J. Zhang, Ruey-Ling Weng, Qingyu Chen, Xiaoqian Jiang, Kirk E. Roberts, Hua Xu