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Natural language processing

As explained in Grokking Machine Learning

Natural language processing is the computational analysis, transformation, or generation of human language.

Natural language processing uses computers to understand, analyze, transform, or generate human language, including spoken and written sentences. section 10.5

Common applications include:

  • Sentiment analysis determines whether a sentence expresses positive or negative sentiment. section 10.5
  • Machine translation converts sentences between languages, while speech recognition turns spoken language into text. section 10.5
  • Text summarization condenses large texts, and chatbots answer questions within specific topics such as customer support. section 10.5

Recurrent neural networks process text by feeding outputs back as inputs, giving the network a form of memory. LSTM and GRU architectures extend this approach. section 10.5

How Grokking Machine Learning covers it

How deep it goes
Defines the field, surveys applications, and gives an intuition for recurrent memory without detailing training or implementation.
Examples
Uses sentiment classification, translation, speech-to-text, text summarization, and topic-specific customer-support chatbots.
What it assumes
Assumes basic familiarity with neural networks while introducing language processing and recurrent memory intuitively.
What you can do after
Evaluate neural-network language applications and recognize when recurrent, LSTM, or GRU architectures are relevant.
How it connects
Connects sentiment analysis, recurrent neural networks, LSTM, GRU, machine translation, speech recognition, summarization, and chatbots.

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Related concepts

This explainer was generated by Manning's knowledge graph from the text of Grokking Machine Learning. The section links point to the parts of the book it draws on.