Natural Language Processing (NLP) is the branch of machine learning that helps computers interpret natural human language.

This might sound familiar –

Hey Siri, set an alarm for 6 AM tomorrow.

Done — your alarm is set for 7 AM tomorrow.

Have you ever wondered how devices like Siri and Alexa understand and interpret your voice? Have you been slightly annoyed when they couldn’t pick up certain terms? The answer is Natural Language Processing (NLP).

NLP is a branch of artificial intelligence that uses both computer science and linguistics to aid computers in understanding “human language.”

The purpose of NLP is to bridge the gap between the human language and the command line interface of a computer.

Humans have hundreds of languages. Computers, on the other hand, have only one native language, which is called machine language.

All of the processes in your computers and smart devices communicate via millions of zeros and ones to perform a specific function. Machine code is unintelligible to humans, which makes NLP a critical part of human-computer interactions.

Let's look at some of the core use cases for NLP and a few real-world applications as well.

Use Cases for Natural Language Processing

Sentiment Analysis

Nowadays, people express their feedback through surveys, customer feedback, and social media platforms.

As a result, organizations have to rely on software that can understand human emotions expressed via text to understand their customer's feedback.

These analyses are used to adapt products and services to meet customer expectations. Sentiment analysis is a crucial tool in achieving this goal.

Sentiment Analysis is the process of identifying opinions expressed in text and understanding whether the author’s attitude towards the discussed product or service is positive, neutral, or negative.

Real-time sentiment analysis helps in identifying critical issues in real-time.

For example, whenever a crisis or a scandal is about to affect an organization due to escalating protests on social media, sentiment analysis models are their to help. Businesses can rely on these models to quickly recognize the issues and get in front of the customer and address it before it blows out of proportion.

Here is an article by MonkeyLearn that explains Sentiment Analysis in depth.

Spam Filtering

NLP is used extensively in spam filtering as well. The function of a spam filter is to spot unwanted e-mails and send them in a separate folder instead of your regular inbox.

Simple spam filtering can be achieved using classification models in machine learning. But NLP is better at distinguishing between useful emails and real-spam.

NLP techniques such as n-gram modelling are applied to emails to classify them as spam or ham with higher accuracy than traditional classification models.

Chatbots

A chatbot is an artificial intelligence (AI) software that can simulate a conversation with a user in natural language. It's an advanced implementation of natural language processing, taking us closer to communicating with computers in a way similar to human-to-human conversations.

Chatbots use a combination of Natural Language Processing, Natural Language Understanding, and Natural Language Generation in order to achieve a Conversational User Interface.

Chatbots can be extremely helpful for customer support, saving businesses time and money. Since the majority of questions raised by customers are asked frequently, they can be handled by chatbots. This helps customer service agents prioritize important customer queries, thereby ensuring overall customer satisfaction.

Products that use NLP

There are numerous products that we use on a daily basis without realizing that they are powered by NLP. Here are some of the most popular ones.

Siri / Alexa / Cortana

Every leading tech giant has developed its own virtual assistant to provide a complete experience to its users.

Alexa, Cortana, Siri, and Google Assistant are used extensively. These programs are not only able to control your smartphone, but also a vast number of compatible smart devices like Air conditioners, Smart TVs, lights, and more.

NLP is an essential part of these virtual assistants. It is used to detect, interpret, and understand the text or voice commands to perform the requested function.

All of these assistants are continually evolving through AI and machine learning to expand the accuracy of their responses.

Grammarly

Grammarly has become one of the most popular writing tools used by people all around the world. It is a fascinating tool that can suggest different kinds of changes in your writing.

Other than checking spelling and grammar, Grammarly can check the usage of active and passive voice, the tone of the document, complexity of sentences, and can suggest changes according to your writing goals.

You've probably wondered how Grammarly works. Contrary to popular belief (that some kind of writing robots are checking your work), AI and NLP are the primary driving forces behind Grammarly’s functions. And it offers different types of features and options for checking your writing.

Grammarly’s AI system is composed of a wide range of NLP algorithms that can deal with different writing styles and tones.

The entire machine learning system behind Grammarly is quite remarkable. It is continuously being updated to become the best writing assistant available on the internet.

NLP allows Grammarly to process English writing and perform various tasks to produce a thorough report. These tasks include writing improvements, readability scoring, sentiment analysis, and suggestions to use alternate words, phrases, and sentence structure.

Google Translate

Language translation is an important application of Natural Language Processing. It has saved organizations billions of dollars in terms of the effort and man-power required in order to translate documents & audio from one language to the other.

Google Translate is perhaps the most popular and efficient translator available. It makes use of AI, machine learning, and NLP to translate text from one language to another. As of now, Google Translate supports 101 languages.

It is important to note that translation is a very tricky process because the software has to understand each word, phrase, and sentence structure for accurate translation.

To tackle these issues, Google Translate is continuously updated to improve the quality and accuracy of the language-translation.

Autocorrect

Spell-check is an underrated tool that is invaluable in our everyday lives. Not everyone can produce a perfect sentence without any spellings or grammar errors. In such a case, spell checks play a huge role in improving writing.

Beyond that, autocorrection is prevalent in smartphones because it allows you to write down your thoughts immediately without worrying about spelling. It (usually) eradicates the spelling errors from your messages and improves communication.

Autocomplete

Autocomplete is another useful application of NLP that is used by almost every web / mobile application, including search engines like Google.

Autocomplete helps you quickly find what you are looking for, using previous searches performed by other customers.

Ultimately, autocomplete searches lead to increased customer satisfaction as they will be able to find the required product, service, or any other information quickly and more accurately.

Similarly, Autosuggest helps with quick e-mailing and messaging. You can select the suitable word or even a phrase with just a single click, all thanks to the advancements in NLP.

We use these features on a daily basis without realizing that they are applications of Natural Language Processing.

Conclusion

There are hundreds of languages in the world that make communication a complex phenomenon. Language also varies from culture to culture.

And there is a lot of diversity in these languages in terms of the writing style, syntax, and grammar rules. Similarly, varying accents and dialects also result in major differences in communication.

NLP is a developing concept that still requires a lot of research and innovation to cater to all kinds of use cases.

Along with deep learning, syntactic and semantic learning are also becoming essential parts of the NLP. They help remove language ambiguities and enhance the quality of NLP-based products and services.

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