Overview of Natural language processing

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Natural language processing playing (NLP)
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• What is NLP?
• Natural language processing (NLP) is an integral
  part of AI, Computer Science, and Linguistics.
  NLP is all about making computers/machines as
  intelligent as human beings in the understanding
  of natural-communication language like text,
  speech, and so on. It comprises 2 major
  functionalities. they are Human to machine
  translation and Machine to Human translation.
• here are 3 different levels of linguistic
  analysis done before performing NLP
• Syntax - What part of the given text is
  grammatically right.
• Semantics - What is the meaning of the given
  text?
• Pragmatics - What is the purpose of the text?

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NLP Techniques that must know    1. Named Entity
Recognition (NER) 2. Tokenization 3. Stemming 4.
Bag of Words 5. Natural language generation 6.
Sentiment Analysis  7. Sentence Segmentation
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• Named Entity Recognition (NER) This technique is
  quite possibly the most important and famous in
  Semantic analysis, Semantics is something
  conveyed by the text. Here the algorithm takes a
  phrase or passage as input and identifies all the
  nouns or names present in that information.
• Tokenization Tokenization is basically splitting
  the whole content into a list of tokens, lists
  can be anything such as words, sentences,
  characters, numbers, punctuation, etc. It is an
  integral part of the Information Retrieval (IR)
  system, it generates tokens respectively that are
  used in the indexing/ranking process.
• Stemming Stemming is a process of reducing words
  from the base root word means it cuts all
  suffixes from the main root word. For example,
  Running becomes Run.
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• Bag of words The bag of words technique is used
  to pre-process text and to extract all the
  features from a text document to use in Machine
  Learning modelling. 
• Natural language generation This technique
  converts raw data into natural plain text for a
  better understanding. 
• Sentiment analysis the main task of this
  technique is to understand the emotion or feeling
  of the written text. It is also known as Emotion
  AI.
• Sentence segments this technique is used to
  identify the text and divide that text into
  meaningful sentences.

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• Key Application Areas of NLP
• We are using NLP every day in Machine learning
  and AI applications. Below are few key
  application areas of NLP. Have a look.
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• Automatic Text Summarizer Given the input text,
  the task is to compose an outline of text
  disposing of unessential points.
• Sentiment-based Text Analysis It is done on the
  given text to anticipate the subject of the
  content, for example, regardless of whether the
  content conveys judgment, opinion, or audits, and
  so on.
• Text Classification It is performed to order
  various journals, news stories according to their
  domain. Multi-report grouping is additionally
  conceivable. A popular illustration of text
  grouping is spam recognition in messages and
  emails. Based on the style of writing in the
  journal, its attribute can be utilized to
  distinguish its writer's name.
• Data Extraction Data extraction is something
  which proposes email programs add occasions to
  the schedule consequently.

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• Till now you have learned about NLP, its
  techniques and application areas. Now its time to
  implement NLP in your Application. If youre
  looking out to integrate NLP in your application
  connect with V2Soft A leading AI service
  provider helps you to design and develop AI
  applications with NLP integration.
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• Related Topics
• Artificial intelligent robots
• Top 6 Industries that uses Chatbot Application
• Testing AI Systems 

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