INK-USC/TriggerNER
TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition (ACL 2020)
This project helps machine learning developers build Named Entity Recognition (NER) models more efficiently. It takes a small amount of 'trigger' annotations for text data and produces a highly accurate NER model. Data scientists and NLP engineers can use this to identify and classify entities like people, organizations, or chemicals from unstructured text.
171 stars. No commits in the last 6 months.
Use this if you need to train a high-performing Named Entity Recognition model but have limited resources for extensive data labeling.
Not ideal if you already have large, conventionally labeled datasets for your NER task, as its core benefit is label-efficient learning.
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Python
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Last pushed
Jun 15, 2022
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