Natural Language Processing And Its Applications

Natural Language Processing And Its Applications
Natural Language Processing And Its Applications
Natural Language Processing And Its Applications

Natural Language Processing (NLP) is an application technique used to analyze natural languages and speech.  Hence it is related to communication between humans and computers. Also how to program the computers to make them analyze and process a huge amount of natural language data. Besides, NLP is one of the branches of in-trend Artificial Intelligence. Its role is to support computer systems in comprehending, manipulating as well as interpreting human language. In simple terms, a computer grasps what humans are communicating with the help of the NLP.

NLP is a solution for the machines to analyze more language-based data as compared to humans. In other words efficient analyzing of text and speech data is possible due to automation. So we cannot avoid many challenges such as voice recognition, natural language understanding, and native language generation because of the huge amount of unstructured data is generated every day.

The first major study took place in a workshop called “Rediscovering 50 years of discoveries in Natural Language Processing” by the Association of Computational Linguistics in the year 2013.

During the statistical revolution of the late 1980s and mid-1990s, a significant part of the Natural Language Processing research has relied on machine learning. Moreover, system-based machine learning use takes place because of the advantages, it has. As an example, the learning procedures which are used during machine learning, focuses on the most common causes, as when we write the rules by hand, then we are not sure of where the effort should be directed.

This is sub-divided into categories for convenience:


  • Grammar Induction
  • Lemmatization
  • Morphological segmentation
  • Part-of-speech tagging
  • Parsing
  • Sentence breaking
  • Stemming
  • Word segmentation
  • Terminology extraction


  • Lexical Semantics
  • Distributional semantics


  • Recognition
  • Segmentation
  • Text-to-speech

Applications Of Natural Language Processing

Machine Translation

Machine translation helps in beating the language barriers that we often encounter while translating technical manuals or catalogs. Therefore the primary challenge of machine translation is, to understand the meaning of the word or sentence before converting.

Automatic Summarization

It is a process that supports decreasing large text documents to a short set of paragraphs or sentences but in a manner that clearly delivers the meaning of the whole content. In other words, automatic summarization is essential to understand the emotional meanings inside pieces of information.

Sentiment Analysis

Sentiment Analysis identifies and determines sentiments and opinions online.  Moreover, the companies use applications like sentiment analysis to understand customer’s thought process and buying behavior of products and services. Therefore this helps in understanding the factors that drive the purchasing decision. In other words sentiment analysis identifies the sentiments among the posts where emotions are not properly expressed.

Text Classification In Natural Language Processing

The goal of text classification is to allot a minimum of one predefined category into a document. It makes managing and sorting of data easier. Moreover, the best example is spam filtering in email.

Natural Language Processing And Its Applications
Natural Language Processing And Its Applications

Question – Answering Of Natural Language Processing

With the improvement of the application for speech understanding technology and voice input, the need for it is increasing. Therefore this is also helping to enhance the usage of question and answer format and is becoming popular due to applications like chat boxes and virtual assistants. Moreover, a question-answer pattern is a system capable of answering a personal request logically.

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