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Top Applications of Natural Language Processing (NLP) in 2024

Top Applications of Natural Language Processing (NLP) in 2024

This blog covers the following topics:

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

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 used for creating an interactive interface between humans and machines. It solves non-linear problems such as processing text and words.

NLP is a machine’s ability to process whatever messages it receives. It structures the information received and determines the accurate response, and then responds in a language that humans can understand. This is how NLP works.

Natural Language Processing Applications

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

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NLP Applications in Business

Natural language processing has many applications in today’s business world. It is one of the most realistic tech trends. Some of the NLP real-time applications in the business field are listed below. Have a look.

Sentiment analysis

  • Natural language processing is used in various functions of sentiment analysis while 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.), which 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
  • It serves as an excellent tool to gain information 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 as well.

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  • Natural language processing helps in the training of chatbots primarily.
  • A chatbot is fed with conversation logs that help it 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 expecting 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 companies hit the right customer by including the right keyword in their text.

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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 determine 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 thus improves diagnosis processes.

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 given below:

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.

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Named entity recognition

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

Part-of-speech tagging

  • In POS tagging, sentences are tagged according to the grammatical structures, such as nouns, adjectives, verbs, etc.
  • This approach uses several tools and techniques, including 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 certain words might have different 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.

Data Science SSBM

Deep Learning Applications of NLP

Deep Learning is a function of Artificial Intelligence that works similar to the human brain in data processing and designing 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.

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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 numerous 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. There are many more to come!

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About the Author

Senior Research Analyst

As a Senior Research Analyst, Arya Karn brings expertise in crafting compelling technical content in Data Science and Machine Learning. With extensive knowledge in AI/ML, NLP, DBMS, and Generative AI, his works get lakhs of views across social platforms that benefit both technical and business spheres.