Alibaba-NLP/KB-NER
Winner system (DAMO-NLP) of SemEval 2022 MultiCoNER shared task over 10 out of 13 tracks.
This project helps natural language processing (NLP) practitioners accurately identify and categorize entities like people, organizations, and locations in text, even across multiple languages. It takes raw text or documents as input and outputs the same text with named entities tagged and classified. This tool is ideal for NLP researchers, data scientists, or language technologists who work with multilingual datasets and need robust named entity recognition capabilities.
186 stars. No commits in the last 6 months.
Use this if you need highly accurate named entity recognition across a variety of languages, including complex or ambiguous entities, and want to leverage external knowledge for better results.
Not ideal if you only need basic, single-language named entity recognition and prefer a simpler, less knowledge-intensive approach.
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186
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21
Language
Python
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Last pushed
Jan 10, 2023
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