How It Works: NLP enables computers to process and understand human language.
- Text Pre-processing: Raw text data (e.g., emails, documents, social media posts) is first cleaned and structured through techniques like tokenization (breaking text into words/phrases) and lemmatization (reducing words to their root form).
- Feature Extraction: Algorithms identify meaningful patterns, words, and phrases, converting them into a format that machines can analyze.
- Model Training: Specialized machine learning models are trained on this processed data to perform specific tasks. For example:
- Sentiment Analysis: Models learn to classify text as positive, negative, or neutral.
- Named Entity Recognition (NER): Models learn to identify and categorize key information like names, organizations, and dates.
- Text Summarization/Generation: Models learn to condense information or create new, coherent text.
- Application & Insights: The trained models are then deployed to automatically process new text inputs, extracting insights, categorizing content, or generating responses, turning unstructured data into actionable intelligence.
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