Natural Language Processing

Natural Language Processing is a subfield of linguistics, AI, and computer science regarding interactions between human language and computers.
Natural Language Processing is a branch of computer science concerned with giving computers the ability to understand and comprehend written text and spoken words the same way humans can.

Natural Language Processing drives computer programs to carry out various commands, such as translating text from one language to another, summarize large pieces of text instantly, or respond to spoken commands.

You may already have interacted with Natural Language Processing in the form of GPS applications, virtual assistants, and speech-to-text software. Natural Language Processing is currently playing a dominant role in enterprise solutions that aid business operations, increase productivity, and simplify critical business-related tasks.

How Natural Language Processing Works?

Natural Language Processing combines computational linguistics with statistical and deep learning models to allow computers to process human language in speech or written text to comprehend its meaning according to the context. Thus, the goal of Natural Language Processing is to achieve human-like language processing.

Natural Language Processing uses Artificial Intelligence (AI) technology to take in inputs, process them and then comprehend them so that the computer can understand them.

Computers have scanning programs to read text and microphones to catch audio. Then, this input data is processed through the computer's Central Processing Unit (CPU). During this processing, the input data is converted to the code that the computer understands.

Natural Language Processing Tools

You can approach and build Natural Language Processing programs with Python programing language. There are other tools as well, for example, the Natural Language Toolkit, which is an open-source collection of programs and educational resources for building Natural Language Processing programs.

You can also build Natural Language Processing programs using algorithms, machine learning, and deep learning models. This was how the earliest Natural Language Processing programs were created and coded. Through this, the program will automatically extract, classify, and label text and voice data and comprehend its meaning by assigning it its most likely meaning according to the context.

Today, machine learning and deep learning models have evolved into Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). They enable Natural Language Processing programs to self-learn as they work and comprehend meaning from large volumes of text and audio.

Applications of Natural Language Processing

Natural Language Processing is a major Artificial Intelligence program behind many modern applications.

1) Detecting Spam

Natural Language Processing is applied in spam detection. Some of the best spam detection technologies on social media platforms, such as Youtube, Discord, and Twitch, use Natural Language Processing. It identifies the overuse of specific text, inappropriate words, and threatening language and blocks them from the platform.

2) Machine and Language Translation

Google Translate is a well-known example that has Natural Language Processing technology at work. It processes the typed text or the spoken words through the microphone and translates it to the computer's coded language. Then, it is translated from the coded language to any other language that the user desires.

An excellent way to test the Natural Language Processing technology in the app is to translate a sentence to another language and then translate it back to see if the original sentence reappears. There used to be many errors like this in the olden days; however, such errors are looked upon and rectified today.

3) Text Summarization

Text summarization software also uses Google speech to text technology to scan and process large volumes of text to create summaries for indexes, databases, or readers who do not have time to read the full text. The best software uses Convolutional Neural Network and Recurrent Neural Network to provide relevant conclusions to the summaries.

4) Virtual Assistants

Virtual assistants, such as Amazon's Alexa and Apple's Siri, are very popular nowadays. They use speech recognition technology through Natural Language Processing to recognize patterns in the speaker's voice. They then process and analyze the input audio data and respond with the appropriate comment or action.  

5) Chatbots

Chatbots are almost the same as virtual assistants, except that they process typed text and respond appropriately. The best chatbots have also learned to recognize contextual clues from the user's use of language, and they use this data to provide even better responses.

6) Speech-to-Text Software

Speech-to-Text software also uses Natural Language Processing as it catches the audio input through the device's microphone and processes it. The spoken words are sent to the central server or the central processing unit, where it uses a vast database to match the processed words. Thus, the correct words are chosen, analyzed, and displayed on the screen.


Natural Language Processing plays a significant part in technology and the way humans interact with it. It has made our lives significantly more manageable with many real-world applications. There are some downsides to it, though, like precision. However, even that has been covered and rectified by many programmers.

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