zjunlp/Low-resource-KEPapers
A Paper List of Low-resource Information Extraction
This is a curated list of research papers focused on 'low-resource information extraction'—meaning extracting valuable facts from text when you have very little training data. It categorizes approaches for tasks like Named Entity Recognition (NER), Relation Extraction (RE), and Event Extraction (EE), including both traditional and modern Large Language Model (LLM)-based methods. Researchers and practitioners in natural language processing will use this to find relevant literature, tools, and datasets for building information extraction systems in data-scarce environments.
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Use this if you are a researcher or practitioner in natural language processing looking for state-of-the-art methods, toolkits, or datasets to extract information from text in domains where labeled data is scarce.
Not ideal if you are looking for a ready-to-use software solution or a tutorial on how to implement information extraction without any prior NLP knowledge.
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Nov 16, 2024
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