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Natural Language Processing

The ability of machines to interpret, understand, and generate human language. It does this by breaking down sentences and paragraphs into small chunks called tokens which it can then statistically analyse.

One minute overview

Natural Language Processing (NLP) is a subfield of Artificial Intelligence (AI) that involves the interaction between computers and human languages. NLP aims to enable computers to understand, interpret, and generate natural language text just like humans do.

To accomplish these tasks, NLP uses a combination of statistical models and rule-based algorithms. Machine learning algorithms, such as deep learning models like neural networks, are used to train the statistical models on large amounts of data, allowing the computer to identify patterns and make predictions. Rule-based algorithms, on the other hand, use hand-crafted rules to analyze and manipulate the text.

NLP involves several processes, including:

  1. Tokenization: This is the process of breaking down a sentence or a paragraph into individual words, phrases, or symbols, known as tokens.
  2. Part-of-speech (POS) tagging: This process involves identifying the part of speech of each token, such as noun, verb, adjective, etc.
  3. Parsing: This is the process of analyzing the sentence structure to determine the relationship between words and phrases.
  4. Named Entity Recognition (NER): This involves identifying and classifying named entities, such as people, places, and organizations, in the text.
  5. Sentiment Analysis: This is the process of determining the emotional tone of a text, whether it is positive, negative, or neutral.
  6. Machine Translation: This involves translating text from one language to another.

To accomplish these tasks, NLP uses a combination of statistical models and rule-based algorithms. Machine learning algorithms, such as deep learning models like neural networks, are used to train the statistical models on large amounts of data, allowing the computer to identify patterns and make predictions. Rule-based algorithms, on the other hand, use hand-crafted rules to analyze and manipulate the text.

Overall, NLP is a complex and evolving field that continues to advance as new techniques and technologies are developed.

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