11. Natural Language Processing (NLP)
Natural Language Processing (NLP) is a crucial branch of Artificial Intelligence that facilitates machine comprehension and generation of human languages. It incorporates elements from linguistics, computer science, and machine learning to enhance applications such as chatbots, translation, and sentiment analysis. Despite its advantages, NLP faces challenges such as ambiguity and the ethical implications of biases in data. Advances in technology continue to enhance the sophistication of NLP in everyday applications.
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What we have learnt
- Natural Language refers to the languages used by humans for communication, characterized by complexity and ambiguity.
- NLP combines linguistics, computer science, and machine learning to process and interpret human languages.
- The NLP pipeline consists of several stages including text acquisition, preprocessing, and various analytical tasks.
Key Concepts
- -- Natural Language
- Any language that humans use for communication, such as English, Hindi, or Spanish, characterized by ambiguity and context dependence.
- -- Natural Language Processing (NLP)
- A field of AI that enables computers to read, understand, and generate human languages.
- -- Natural Language Understanding (NLU)
- The component of NLP focused on enabling machines to understand and interpret human input.
- -- Natural Language Generation (NLG)
- The component of NLP focused on generating human-like responses or texts based on inputs.
- -- NLP Pipeline
- The series of stages through which text data is processed, including text acquisition, preprocessing, and analysis.
- -- Deep Learning Methods
- Advanced techniques using neural networks for various NLP tasks like sequence modeling and word embeddings.
- -- Ethical Considerations in NLP
- Concerns regarding bias in data, privacy, misinformation, and potential misuse of NLP technologies.
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