6 Real-World Examples of Natural Language Processing

Major Challenges of Natural Language Processing NLP

natural language processing examples

Therefore, it is considered also one of the best natural language processing examples. For making the solution easy, Quora uses NLP for reducing the instances of duplications. And similarly, many other sites used the NLP solutions to detect duplications of questions or related searches. And this is how natural language processing techniques and algorithms work. And this is not the end, there is a list of natural language processing applications in the market, and more are about to enter the domain for better services. Search engines are the next natural language processing examples that use NLP for offering better results similar to search behaviors or user intent.

Many enterprises are looking at ways in which conversational interfaces can be transformative since the tech is platform-agnostic, which means that it can learn and provide clients with a seamless experience. Infuse powerful natural language AI into commercial applications with a containerized library designed to empower IBM partners with greater flexibility. This content has been made available for informational purposes only.

Complete Guide to Natural Language Processing (NLP) – with Practical Examples

It’s an intuitive behavior used to convey information and meaning with semantic cues such as words, signs, or images. It’s been said that language is easier to learn and comes more naturally in adolescence because it’s a repeatable, trained behavior—much like walking. That’s why machine learning and artificial intelligence (AI) are gaining attention and momentum, with greater human dependency on computing systems to communicate and perform tasks. And as AI and augmented analytics get more sophisticated, so will Natural Language Processing (NLP). While the terms AI and NLP might conjure images of futuristic robots, there are already basic examples of NLP at work in our daily lives.

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It is the technology that is used by machines to understand, analyse, manipulate, and interpret human’s languages. It helps developers to organize knowledge for performing tasks such as translation, automatic summarization, Named Entity Recognition (NER), speech recognition, relationship extraction, and topic segmentation. Every day, humans exchange countless words with other humans to get all kinds of things accomplished. But communication is much more than words—there’s context, body language, intonation, and more that help us understand the intent of the words when we communicate with each other. That’s what makes natural language processing, the ability for a machine to understand human speech, such an incredible feat and one that has huge potential to impact so much in our modern existence.

Eight great books about natural language processing for all levels

This will not just help users but also improve the services rendered by the company. This brings numerous opportunities for NLP for improving how a company should operate. When it comes to large businesses, keeping a track of, facilitating and analyzing thousands of customer interactions for improving services & products. In any of the cases, a computer- digital technology that can identify words, phrases, or responses using context related hints.


For example, the Loreal Group used an AI chatbot called Mya to increase the efficiency of its recruitment process. Organizations in any field, such as SaaS or eCommerce, can use NLP to find consumer insights from data. Such features are the result of NLP algorithms working in the background.

What is natural language processing with examples?

The proposed test includes a task that involves the automated interpretation and generation of natural language. However, there is still a lot of work to be done to improve the coverage of the world’s languages. Facebook estimates that more than 20% of the world’s population is still not currently covered by commercial translation technology.

natural language processing examples

Earlier iterations of machine translation models tended to underperform when not translating to or from English. Entity recognition helps machines identify names, places, dates, and more in a text. In contrast, machine translation allows them to render content from one language to another, making the world feel a bit smaller. By understanding NLP’s essence, you’re not only getting a grasp on a pivotal AI subfield but also appreciating the intricate dance between human cognition and machine learning.

It is also used by various applications for predictive text analysis and autocorrect. If you have used Microsoft Word or Google Docs, you have seen how autocorrect instantly changes the spelling of words. With NLP-based chatbots on your website, you can your visitors are saying and adapt your website to address their pain points. Furthermore, if you conduct consumer surveys, you can gain decision-making insights on products, services, and marketing budgets.

  • NER can be implemented through both nltk and spacy`.I will walk you through both the methods.
  • Basic NLP tasks include tokenization and parsing, lemmatization/stemming, part-of-speech tagging, language detection and identification of semantic relationships.
  • On the other hand, data that can be extracted from the machine is nearly impossible for employees for interpreting all the data.
  • At the intersection of these two phenomena lies natural language processing (NLP)—the process of breaking down language into a format that is understandable and useful for both computers and humans.
  • Publishers and information service providers can suggest content to ensure that users see the topics, documents or products that are most relevant to them.

From predictive text to data analysis, NLP’s applications in our everyday lives are far-ranging. Take for example- Sprout Social which is a social media listening tool supported in monitoring and analyzing social media activity for a brand. The tool has a user-friendly interface and eliminates the need for lots of file input to run the system. This is how an NLP offers services to the users and ultimately gives an edge to the organization by aiding users with different solutions.

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natural language processing examples

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