Natural Language Processing(NLP)

Artificial Intelligence Tutorial


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Natural Language Processing(NLP) : Introduction

  • Natural Language Processing is the branch of computer science and artificial intelligence.

  • It deals with the human language and computers all together in order to process the human language and draw out some meaning from it, so that the result can be further used.

  • The Natural Language Processing can have speech and written test as Input as well as Output in the following combinations;

    1. Input: Speech , Output: Text.

    2. Input: Text, Output: Speech.

  • Natural Language Processing is mainly comprises up of two components:

    1. Natural Language Understanding(NLU).

    2. Natural Language Generation(NLG).

 

Natural Language Understanding

  • NLU is much harder to implement as compared to NLG.

  • It is because of the fact that, NLU is designed in such a manner that it is able to handle unstructured and random inputs. It then, convert those inputs into structured form so that machines can understand it and can handle it to generate some predictable and meaningful outputs.

  • NLU process is carried out before NLG process.

Natural Language Generation

  • With the help of NLG, Unknown internal representations can be converted into meaningful phrases and then into sentences.

  • Natural Language Generation can also be called as a translator which can translate data from human language.

 

Natural Language Processing Works : Working

  • Every Natural Language Processing system goes through the five step process as described below.

 

 

This image describes the complete flow of architecture of natural language processing. All the five steps are describes here.

Natural Language Processing : Flow

 

  1. Lexical Analysis: This phase analysis the structure of the words. It breaks the whole lot of paragraphs into  simple phrases and phrases into even more simpler words.

  2. Syntactic Analysis: This phase rephrases the series of words generated in the lexical analysis phase and combines them in such a way to generate meaningful sentences and paragraphs.

  3. Semantic Analysis: This phase extracts the meaning and checks the meaningfulness of the sentences. For Example: “Wet water”.

  4. Discourse IntegrationThis phase draws out the meaning of sentences or phrases currently present on the basis of previous and next sentences or phrase.

  5. Pragmatic Analysis: This phase is responsible for extracting the actual meaning of the phrases by comparing it with real world entities.