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NER - Coggle Diagram
NER
Workflow for NER
Data Collection : Gather raw text data (e.g., articles, reviews, tweets).
Preprocessing : Clean and tokenize the text.
Entity Annotation : Label entities in the dataset for training.
Model Training : Train a model using annotated data.
Evaluation : Assess performance using metrics like precision, recall, and F1-score.
Deployment : Integrate the model into real-world applications
Applications
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• Customer Feedback Analysis : Identify product names, brands, and issues in reviews.
• Healthcare : Extract patient names, diseases, and treatments from medical records.
• Legal Documents : Identify laws, cases, and parties in legal texts.
• News Analysis : Track mentions of people, organizations, and events in news articles
Challenges
• Ambiguity : Words with multiple meanings (e.g., "Apple" as a fruit vs. a company).
• Context Sensitivity : Entities depend on surrounding text (e.g., "Washington" could refer to a person, location, or organization).
• Language Variations : Handling slang, abbreviations, and multilingual texts.
• Domain-Specific Entities : Pre-trained models may fail in specialized domains like medicine or law.
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Approaches
• Rule-Based Methods :
• Use predefined rules and dictionaries to identify entities.• Example: Matching patterns like "Mr. John" for names.
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• Deep Learning Models :
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• Popular Architectures:
• Recurrent Neural Networks (RNNs), LSTMs.
• Transformers (e.g., BERT, RoBERTa).
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What is ?
• Named Entity Recognition (NER) is a subtask of Natural Language Processing (NLP) that identifies and classifies key entities in text into predefined categories.
• Examples: Names of people, organizations, locations, dates, etc.