yyDing1/GNER
[ACL 2024 Findings] Code implementation of Paper "Rethinking Negative Instances for Generative Named Entity Recognition"
This project helps anyone who works with text data automatically extract specific information like names, locations, or dates from unstructured sentences. You input a sentence and a list of entity types you're interested in, and it outputs the sentence with each word labeled by its entity type (e.g., 'George Clooney' as 'actor'). This is useful for data scientists, NLP engineers, or researchers building systems to understand and organize large volumes of text.
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Use this if you need to perform Named Entity Recognition (NER) on text, especially if you want strong performance even on new types of entities or domains you haven't explicitly trained on.
Not ideal if your primary goal is simple keyword extraction or if you need to classify entire documents rather than token-level entities within sentences.
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60
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3
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 20, 2024
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