c3sr/split-ner
PyTorch code for paper, 'Split-NER: Named Entity Recognition via Two Question-Answering-based Classifications', ACL'23
This project helps natural language processing practitioners accurately identify and categorize key entities within unstructured text. You provide raw text documents, and it outputs the identified entities along with their specific types (e.g., person, organization, location). It's designed for data scientists, NLP engineers, or researchers who need precise entity extraction for various analytical or operational tasks.
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Use this if you need to extract specific types of information from text with high accuracy and are working with diverse or specialized datasets, such as those found in cybersecurity or biomedical research.
Not ideal if you primarily need general text understanding without specific entity categorization, or if your primary goal is rapid, large-scale processing where maximum precision isn't the top concern.
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Last pushed
Nov 07, 2023
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