yahshibu/nested-ner-tacl2020-flair
Implementation of Nested Named Entity Recognition using Flair
This project helps natural language processing researchers train models to identify complex, overlapping named entities in text. It takes raw text from datasets like ACE-2004 or GENIA and common word embeddings as input, then outputs a trained model capable of recognizing these 'nested' entities. NLP researchers or computational linguists working on advanced information extraction or text understanding would find this useful.
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Use this if you are an NLP researcher aiming to experiment with or replicate state-of-the-art methods for nested named entity recognition.
Not ideal if you need a ready-to-use tool for identifying entities in text without developing or training a custom model.
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Language
Python
License
GPL-3.0
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Last pushed
Oct 29, 2021
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