ajitrajasekharan/unsupervised_NER
Self-supervised NER prototype - updated version (69 entity types - 17 broad entity groups). Uses pretrained BERT models with no fine tuning. State-of-art performance on 3 biomedical datasets
This project helps scientists, researchers, and analysts automatically identify and extract specific types of entities, like diseases or chemicals, from text documents. You provide raw text, and it returns the same text with key phrases and terms highlighted and categorized. This is ideal for anyone working with large volumes of unstructured text in fields like biomedicine who needs to quickly pinpoint and organize specific information.
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Use this if you need to automatically extract a wide range of specific named entities from scientific or general text without having to manually label a large dataset for training.
Not ideal if you primarily need to identify all noun phrases in a sentence rather than specific entity types, as this requires additional setup and dependencies.
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78
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Language
Python
License
MIT
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Last pushed
Jul 16, 2022
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