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
High Accuracy Location Information Extraction from Social Network Texts Using Natural Language Processing
Lossan Bonde, Severin Dembele
The Smart Data Extractor, a Clinician Friendly Solution to Accelerate and Improve the Data Collection During Clinical Trials
Sophie Quennelle, Maxime Douillet, Lisa Friedlander, Olivia Boyer, Anita Burgun, Antoine Neuraz, Nicolas Garcelon
Data-Driven Information Extraction and Enrichment of Molecular Profiling Data for Cancer Cell Lines
Ellery Smith, Rahel Paloots, Dimitris Giagkos, Michael Baudis, Kurt Stockinger
CollabKG: A Learnable Human-Machine-Cooperative Information Extraction Toolkit for (Event) Knowledge Graph Construction
Xiang Wei, Yufeng Chen, Ning Cheng, Xingyu Cui, Jinan Xu, Wenjuan Han
A Human-in-the-Loop Approach for Information Extraction from Privacy Policies under Data Scarcity
Michael Gebauer, Faraz Maschhur, Nicola Leschke, Elias Grünewald, Frank Pallas
PIVOINE: Instruction Tuning for Open-world Information Extraction
Keming Lu, Xiaoman Pan, Kaiqiang Song, Hongming Zhang, Dong Yu, Jianshu Chen
InteractiveIE: Towards Assessing the Strength of Human-AI Collaboration in Improving the Performance of Information Extraction
Ishani Mondal, Michelle Yuan, Anandhavelu N, Aparna Garimella, Francis Ferraro, Andrew Blair-Stanek, Benjamin Van Durme, Jordan Boyd-Graber