Top Applications of NLP in 2020 – Intellipaat

What is all about NLP? How does NLP work? What are the applications of NLP? You will get answers to all these questions in this blog on the applications of natural language processing. Here, you will see various NLP applications in different sectors such as business, healthcare, text mining, Deep Learning, etc. Read on!

  • Updated on: 30th Apr, 20
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What is Natural Language Processing (NLP)? 

Before moving forward to check out the applications of NLP, first, let us understand what NLP is and how it works. What is NLP?

Natural language processing (NLP) is an emerging technology, which is a mixture of Computational Linguistics and Artificial Intelligence. It is been used for creating an interactive interface between humans and machines that continues to be in a leading position as of now and in the future. It solves non-linear problems such as processing text and words.

NLP is a machine’s ability to process what has been said. It structures the information received and determines the accurate response, and then responds in a language that we can understand. This is how NLP works.

This blog covers the following topics such as:

Natural Language Processing Applications

Now that we know what NLP is and how it works, let’s come back to our topic of discussion, i.e., various applications of NLP in different fields.

NLP Applications in Business

Natural language processing has many applications in today’s business world. It is one of the most realistic tech trends. These are some of the NLP real-time applications.

Sentiment analysis:

  • Natural language processing is used in various functions of sentiment analysis by monitoring social media.
  • Sentiment analysis is implemented on a set of data by adding reviews to the dataset and labeling 1 for ‘positive’ and 0 for ‘negative.’
  • It identifies the mood of a message (such as happy, sad, angry, sleepy, etc.) that is implemented by a combination of natural language processing and statistics.
  • It also helps organizations get feedback from customers so that they can enhance their products. 

Customer service:

  • Natural language processing helps in various functions of customer service, and it serves as an excellent tool to gain observations on preferences, approaches, and audience tastes. For instance, customers’ feedbacks are recorded to know whether they are happy or not and what requirements they need in the future.
  • Speech separation in AI helps in identifying the voices of each speaker and answers each caller separately.
  • It is an outstanding system that converts text into speech that can help blind people.

Chatbots: 

  • Natural language processing helps in various functions of chatbots such as in training them.
  • A chatbot is fed with conversation logs that help the chatbot understand what type of answer should be given as a reply to what type of question. 
  • Chatbots can also understand wit, sarcasm, and other conversational tones with the help of NLP.
  • In the future, we are going to have intelligent chatbots that will offer personalized assistance to customers.

Managing advertisement channels:

  • In this application, natural language processing implements keyword matching, which is used in managing advertisements.
  • It helps in collecting information such as: what are the needs of customers? Where do the customers look for to fulfill their needs? What are the products they are looking for?
  • Natural language processing helps hit the right customers by matching the right keywords in their text.

NLP Applications in Healthcare

  • Natural language processing helps enhance the completeness and accuracy of electronic health records by transforming free text into standardized data. 
  • NLP helps analyze patients and determines the complexities of phenotyping that is useful for physicians. 
  • NLP algorithms help in Identifying potential errors in healthcare delivery that aids healthcare organizations (HCO) to keep track.
  • NLP predictive analysis helps in identifying high-risk patients and also for improved diagnosis processes.

To get a clear understanding of Natural Language Processing check out our video

NLP Applications in Web Mining

Web mining is a technique that helps extract useful information from the data gathered from the Internet. It uses traditional data mining techniques to extract information from the internet. It is classified into three types; they are as follows:

  • Web usage mining: It involves mining web server logs.
  • Web structure mining: It identifies the relationship between web pages and their links.
  • Web content mining: It deals with the content of the web.

NLP applications in web mining for social media are as follows:

Automation summarization:

  • Automation summarization is a technique that cuts down a large text format into a meaningful short paragraph.
  • This technique is used by search engines.

Named entity recognition:

  • Name entity recognition identifies physical objects and entities such as names of people, places, and organizations in the text.
  • It uses historical data patterns to identify new name entities.

Part-of-speech tagging:

  • In this approach, sentences are tagged according to the grammatical structures such as nouns, adjectives, verbs, etc.
  • Part-of-speech tagging uses several techniques such as lexicons, rules, and dictionaries.

Word-sense disambiguation:

  • It is a natural language processing approach that identifies the correct sense of a word in a sentence as in the word could have many meanings in different contexts.
  • Following the theories of human linguistic memory, WordNet is built, which is a library used in word-sense disambiguation. It is a free lexical database in English that contains a large collection of words and their meanings provided in the form of word-sense pairs.

Deep Learning Applications in NLP

Deep Learning is a function of Artificial Intelligence that works similar to the human brain in data processing and design patterns that are used in decision-making. It is also a subset of Machine Learning, which has a network capable of learning unsupervised or unstructured data.

  • Machine translation: Machine translation is the automatic translation of speech or text into another language, e.g., translating a text document from French to English.
  • Language modeling: Language modeling is used to predict a new statement or a word from the existing data. It is also used for handwriting recognition, spelling correction, etc., e.g., generating headlines for news articles.
  • Caption generation: This approach describes the content of an image, e.g., generating captions for images and videos.
  • Question answering: This system helps in answering user queries that involve noun phrases—such as ‘Who is the president of the USA?’; ‘When was the Berlin Wall destroyed?’; ‘restaurants near me,’ and so on—and questions regarding medical records, news articles, best tutorials, etc

NLP Applications in Text Mining

Text mining is a process of extracting high-quality information from text. It is also known as text data mining. Different NLP applications in text mining are:

  • Text recognition 
  • Customer service
  • Personalized bots
  • Sentiment analysis

Wrapping It up!

Today, natural language processing has become a great problem solver in morphological word processing, and it solves classification problems. NLP technologies still face complex problems in some difficult areas. In this blog, we have discussed the prevailing NLP applications in various fields such as business, healthcare, web mining, text mining, etc.

if you are an NLP enthusiast, enroll now with Intellipaat’s NLP in Python Certification Training Course.

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