https://github.com/trilokida/named-entity-recognition-and-classification
Given a string statement, the aim is to identify the B-Protein entity in the statement.
https://github.com/trilokida/named-entity-recognition-and-classification
bagging-ensemble classification dictvectorizer ensemble-learning machine-learning naive-bayes-classifier named-entity-recognition ner random-forest scikit-learn
Last synced: 4 months ago
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Given a string statement, the aim is to identify the B-Protein entity in the statement.
- Host: GitHub
- URL: https://github.com/trilokida/named-entity-recognition-and-classification
- Owner: TrilokiDA
- Created: 2020-09-29T10:19:57.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2020-09-29T10:27:27.000Z (almost 5 years ago)
- Last Synced: 2025-01-17T03:16:52.958Z (6 months ago)
- Topics: bagging-ensemble, classification, dictvectorizer, ensemble-learning, machine-learning, naive-bayes-classifier, named-entity-recognition, ner, random-forest, scikit-learn
- Language: Jupyter Notebook
- Homepage:
- Size: 9.77 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Named-Entity-Recognition-and-Classification
### Named-entity recognition
Named-entity recognition (NER) (also known as (named) entity identification, entity chunking, and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.[wiki](https://en.wikipedia.org/wiki/Named-entity_recognition)