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Natural Language Processing (NLP)

What is natural language processing with example?

Natural Language Processing (NLP) is a branch of artificial intelligence that enables computers to understand, interpret, and generate human language. For example, when you ask a voice assistant like Siri or Alexa a question and it responds accurately, that’s NLP in action.

What are the 5 steps in NLP?

  1. Tokenization: Segmenting text into words, phrases, or other meaningful elements.
  2. Parsing: Analyzing the grammatical structure of a sentence to understand its components.
  3. Semantic Analysis: Determining the meaning of the text.
  4. Pragmatic Analysis: Understanding the context and intent behind the text.
  5. Sentiment Analysis: Identifying the mood or emotion conveyed by the text.

Why is NLP used?

NLP is used to facilitate human-computer interactions, allowing machines to understand and respond to user inputs in natural language. It helps in automating tasks, extracting insights from vast amounts of textual data, and enhancing user experiences across various digital platforms.

Where is NLP used?

NLP is used in a variety of domains, including:

  • Search Engines: To improve search results based on user queries.
  • Voice Assistants: To interpret and respond to voice commands.
  • Chatbots: To interact with users in customer support or information retrieval.
  • Text Analytics: To extract insights from large datasets, like reviews or social media posts.
  • Machine Translation: To translate text from one language to another.

How is NLP used today?

Today, NLP is widely used in:

  1. Content Recommendation: Platforms like Netflix or YouTube use NLP to analyze user preferences and recommend content.
  2. Sentiment Analysis: Brands use NLP to gauge public sentiment about their products based on reviews or social media mentions.
  3. Automated Customer Support: Many companies employ chatbots to handle basic customer queries without human intervention.
  4. Speech Recognition: Transcribing spoken language into text, as seen in voice-to-text applications.