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
Instruct and Extract: Instruction Tuning for On-Demand Information Extraction
Yizhu Jiao, Ming Zhong, Sha Li, Ruining Zhao, Siru Ouyang, Heng Ji, Jiawei Han
MuLMS: A Multi-Layer Annotated Text Corpus for Information Extraction in the Materials Science Domain
Timo Pierre Schrader, Matteo Finco, Stefan Grünewald, Felix Hildebrand, Annemarie Friedrich
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