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Natural Language Processing

Natural Language Processing (or NLP) is the field of artificial intelligence that aims to enable computers to comprehend written and spoken language in a manner that is similar to that of humans.

NLP blends statistical, machine learning, and deep learning models with computational linguistics—rule-based modelling of human language. With the use of these technologies, computers are now able to process human language in the form of text or audio data and fully "understand" what is being said or written, including the speaker's or writer's intentions and sentiment. It has numerous practical uses in a wide range of industries, including corporate intelligence, search engines, and medical research.


NLP has two components:

  • Natural Language Understanding (NLU): It involves converting the provided natural language input into helpful representations and examining the language's various facets.

  • Natural Language Generation (NLG): Relatively more straightforward than NLU, it involves converting some internal representation into meaningful words, phrases, and sentences that may be expressed in natural language.


Applications of NLP

The following are some of the most common tasks carried out by natural language processing algorithms:


  • Text Extraction: This entails automatically extracting key information from text and summarising it. One illustration of this is keyword extraction, which takes the text's most significant terms and can be helpful for search engine optimization. Named entity recognition is another illustration; it picks out names of persons, locations, and other entities from text.


  • Text Summarization: Text summarization uses NLP approaches to process massive amounts of digitized text and provide summaries and synopses for indexes, research databases, etc.


  • Text Classification: This entails giving texts labels in order to categorise them. This may be helpful for sentiment analysis, which aids the NLP system in figuring out the emotion or sentiment behind a text. It can also be helpful for intent detection, which aids in predicting what the speaker or writer may do based on the material they are composing.


  • Machine Translation: This involves translating text between languages without any human intervention. As opposed to simply replacing one word with another, translation must precisely capture the meaning and tone of the source language in order to produce material that has the same meaning and the desired effect in the target language.


  • Virtual Assistants and Chatbots: Speech recognition and natural language generation are used by virtual assistants like Apple's Siri, Samsung's Bixby, and Amazon's Alexa to identify patterns in voice commands and to respond with the appropriate action or helpful comments. Chatbots work the same in response to typed text input.



Phases of NLP

NLP is executed in the following five phases:

  • Lexical or Morphological Analysis: It entails recognizing and examining word structures. The lexicon of a language is the entire corpus of words and expressions. The entire text is broken down into paragraphs, sentences, and words by lexical analysis.
The source code is scanned as a stream of characters and transformed into readable lexemes. The link between these morphemes is determined by lexical analysis, which also changes the word into its root form. A lexical analyzer also assigns the word's probable parts of speech (POS).


  • Syntax (Syntactic) Analysis or Parsing: Syntactic or syntax analysis a method for examining links between words, arranging words, and evaluating grammar. It requires looking at the syntax of the phrase's words and arranging them to show how they relate to one another. Based on the sentence structure and the likely POS produced in the previous stage, a syntax analyzer gives POS tags.

For example, “He are standing.”

This sentence is grammatically incorrect and makes no sense; hence it will be rejected by the syntactic analyzer.


  • Semantic Analysis: The technique of determining the meaning of a statement is known as semantic analysis. The attention is primarily on the literal meaning of words, phrases, and sentences. It also has to do with stringing words into coherent sentences. It takes the precise meaning or dictionary definition from the text. The task domain's syntactic structures and objects are mapped to do this.

For example, “The Taj Mahal ate Sakina.”

This sentence is grammatically correct but is illogical since it does not mean anything.


  • Discourse Integration: Discourse integration concerns the application of context to a statement. Any sentence's meaning is defined by the meaning of the sentence that comes before it. It also establishes the meaning of the subsequent statement.

For example, “I sat there.”

Here, the word ‘there’ could refer to anything, and we require a preceding sentence to make sense of the current statement.


  • Pragmatic Analysis: Pragmatic analysis focuses on determining the intended effect of the text by using a set of guidelines that define cooperative discussions. It involves deciding those features of language that demand an understanding of the real world. It is used to determine the tone and inflection of the given statement.

For example, whether “pass the popcorn” is an order or a request is determined by pragmatic analysis.



Libraries and APIs for Implementation of NLP

The following list contains the most commonly used libraries and APIs (Application Programming Interfaces) you will encounter:


Libraries

  • Scikit-learn
  • Natural Language Toolkit (NLTK)
  • Pattern
  • Quepy


APIs

  • Speech to Text API
  • Google Cloud Natural Language API
  • Chatbot API
  • IBM Watson API


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