speedcell4/nersted
Official implementation of "Nested Named Entity Recognition via Explicitly Excluding the Influence of the Best Path" (ACL'21)
This project helps natural language processing researchers evaluate and compare different strategies for identifying complex, overlapping entities within text. You provide annotated text datasets like ACE2004, ACE2005, or GENIA, and it outputs performance metrics for nested named entity recognition. It's designed for researchers focused on advancing methods for more precise information extraction from unstructured text.
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Use this if you are an NLP researcher working on advanced named entity recognition and want to experiment with different decoding strategies for nested entities.
Not ideal if you are looking for an out-of-the-box solution to extract simple, non-overlapping entities from text or if you are not comfortable working with command-line Python scripts and data preprocessing.
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Sep 13, 2022
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