GanjinZero/Triaffine-nested-ner
Fusing Heterogeneous Factors with Triaffine Mechanism for Nested Named Entity Recognition [ACL 2022 Findings]
This project helps natural language processing researchers extract complex, overlapping entities from text, such as medical terms or company names that contain other named entities. It takes raw text as input and outputs a structured list of identified nested entities. This tool is for NLP researchers and machine learning engineers who need to improve the accuracy of information extraction from specialized documents.
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Use this if you are an NLP researcher working on named entity recognition and need to accurately identify entities that are embedded within other entities, a common challenge in domains like biomedicine or legal text.
Not ideal if you are looking for a simple, out-of-the-box solution for basic, non-overlapping named entity recognition without needing to engage with model training or complex data preparation.
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Python
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Last pushed
Feb 21, 2023
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