The Use Cases of Natural Language Processing

Human-machine interactions enabled by AI are nothing new. For a long time, governments and corporations have used data science and machine learning technologies. Natural language processing is one of the most rapidly advancing AI technologies today (NLP). However, there are still many people who do not understand about NLP, including the implementation of the use case. Want to learn more? Check out the following post.
April 1, 2022

What is Natural Language Processing?

Natural language processing (NLP) is a type of artificial intelligence (AI) that recognizes and understands natural human languages. NLP techniques are used to translate written or spoken human speech into a format that computers can understand.

The majority of us utilize NLP business apps on a daily basis without even realizing it. Natural language processing technology is used in practically all spell-checkers, online search, translators, and voice assistants. Here's a rundown of the numerous NLP jobs that modern NLP software can perform.

We use machines by teaching them how to understand human words via Natural Language Processing. We essentially use text data and have computers evaluate and process vast amounts of it. In today's environment, such data is in high demand because it provides a wealth of information and insight into corporate operations and profitability.

Here are some use cases related to Natural Language Processing.

Speech to text

Natural Language Processing (NLP) speech to text is a powerful Deep Learning application that allows machines to understand and read human language with the intent to respond and react like people do. The core idea behind NLP is to provide human language into intelligent  systems as data for them to consider and then use in many fields.

The following steps is to make up the primary process of the speech-to-text system.

  • Uploading an audio file, a live speech from a microphone, or a recorded voice (audio data).
  • The process of translating sound into electrical impulses is the next step (feature engineering).
  • The signal is converted into digital data using an analog-to-digital converter (input).
  • Transcribing audio(data) into text using a specified model (output).

Converting speech to text is one of the implementations of Natural Language Processing. The text to voice conversion of the words we say (in short, the sounds we create) into the words we read (the text block we get on our computer screen or maybe a piece of paper) can be assisted by NLP using Deep Learning.

This process can accomplish a lot with unstructured text data by discovering patterns of sentiments, main words used for specific scenarios, and specific text slates inside a block of text using deep learning algorithms for text to speech, namely Neural Networks.

Grammar Checking Software

Grammar checkers are one of the most commonly used NLP applications. These tools look for mistakes in our content and make ideas for how to fix them. These technologies are already programmed with information on proper grammar and can distinguish between correct and inappropriate usage.

One of the most popular tools in this case is Grammarly. Grammar checkers are extremely useful and beneficial to everyone. They can be used by anyone, from schoolchildren to senior executives, to improve the quality and clarity of their writing. The quality of writing improves dramatically with grammatical tools, and many people are interested in the paid edition of the product, resulting in increased revenue.

Grammarly is a grammar checker that works in the cloud. According to them, their team of linguists and deep learning technologists create algorithms that analyze millions of phrases from study text to discover the norms and patterns of effective writing.

It also learns from data; every time a user accepts or rejects a Grammarly suggestion, the AI improves. The AI's exact workings aren't known, but it's clear that it employs a lot of NLP approaches.

Analytical Text

The practice of extracting meaningful data and insights from text data is known as text analytics. Customer product reviews, chatbot data, customer suggestion mailings, and other text data are examples of text data that businesses have at their disposal.

Text analytics may be used to decipher and recognize data trends, as well as make commercial decisions. Word/phase-frequency computation, word cloud production, sentiment analysis, and other methods are among them. It also provides a convenient means of working with big amounts of text data. The source text or documents are processed in the text analytics process, and then various NLP approaches are applied to them. To begin, all unnecessary punctuation and symbols have been deleted.

Stopwords must then be eliminated. Stopwords are the most prevalent words in any language, and they frequently convey no emotion. In English, stopwords include and, on, the, that, at, is, in, and so on. To eliminate these stopwords from our text, we can use a variety of methods and algorithms. Cleaning the text for analytics also includes stages such as stemming and lemmatization. The text is now ready to be processed and worked on after using all of the fundamental NLP techniques.


A solid customer helpline and support network are essential for businesses. Chatbots are an important aspect of a well-functioning customer care system. Virtual assistants and chatbots are now commonplace in most online services and apps.

Natural Language Generation capabilities enable chatbots to converse with human customers or clients and solve or understand their problems before a human executive can intervene. Chatbots are pre-programmed with potential inquiries and responses. Duolingo, for example, has effective chatbots that can answer a wide range of questions.

Chatbots are extremely effective in capturing leads and converting them into paying clients. Chatbots are becoming more cost-effective and easier to interact with as technology improves. Text input in the form of user requests and doubts is provided to the chatbot. Different chatbots work in different ways, but they all employ a database or algorithm to provide a response that addresses a business problem when the user input is delivered.

Text Classification

This problem is a so-called text classification from a methodological standpoint. A prognosis for a previously defined target variable is made based on a text. To train the model, the data, in this case medical documents, must be annotated with the target variable, as is standard in supervised learning. Because a categorization problem (appropriate or unsuitable research participants) must be handled here, the experts manually analyze the suitability of some people in the pool for the study. The model may now understand the correlations between a person's medical records and their appropriateness based on these training instances.

Sentiment analysis is a popular method of text classification. This entails categorizing texts into predetermined emotion groups (for example, negative or positive). This data is especially valuable in the financial realm and for monitoring social media. Text categorization can also be utilized in a variety of situations when it's important to arrange documents by category (e.g., invoices, letters, reminders).


Several different NLP (“Natural Language Processing”) tasks can now be solved with outstanding quality. Natural language processing (NLP) is undeniably a topic that has gotten a lot of interest in the Big Data world recently. NLP is not just a fascinating topic of study, but also a technology whose use in the commercial world is expanding all the time. NLP will not only become a basis of a data-driven corporate culture in the future, but it also has significant innovation potential through direct application, which is worth investing in. 

Written by Denny Fardian
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