Natural Language Processing, NLP
Natural Language Processing-NLP

In today’s fast-growing technologies the natural language processing playing a vital role to identify natural language-based data. This blog will cover What is NLP, NLP techniques, and key application areas of NLP.

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?

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

 

  • 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.
  • 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.

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.

  • 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.


Till now you have learned about NLP, its techniques and application areas. Now its time to implement NLP in your Application. If you’re 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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