ljynlp/W2NER
Source code for AAAI 2022 paper: Unified Named Entity Recognition as Word-Word Relation Classification
This project helps identify and categorize important terms like names, places, or organizations within text, even when they are complex, overlapping, or broken up. You input raw text, and it outputs a precise list of identified entities and their types. This is useful for anyone working with textual data who needs to extract structured information, such as researchers analyzing scientific papers or professionals reviewing reports.
552 stars. No commits in the last 6 months.
Use this if you need highly accurate extraction of diverse named entities from text, including complex cases where entities might overlap or be discontinuous.
Not ideal if you are looking for a simple keyword extraction tool or don't need to differentiate between flat, nested, and discontinuous entities.
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552
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85
Language
Python
License
MIT
Category
Last pushed
Jul 14, 2022
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