8 NLP Examples: Natural Language Processing in Everyday Life

Although sometimes tedious, this allows corporations to filter customer information and quickly get you to the right representative. These machines also provide data for future conversations and improvements, so don’t be surprised if answering machines suddenly begin to answer all of your questions with a more human-like voice. NLP business applications come in different forms and are so common these days. For example, spell checkers, online search, translators, voice assistants, spam filters, and autocorrect are all NLP applications. They can also be used for providing personalized product recommendations, offering discounts, helping with refunds and return procedures, and many other tasks.

• Smart assistants, which were once in the realm of science fiction, are now commonplace.
• BERT has become a popular tool in NLP data science projects due to its superior performance, and it has been used in various applications, such as chatbots, machine translation, and content generation.
• Before deep learning-based NLP models, this information was inaccessible to computer-assisted analysis and could not be analyzed in any systematic way.

Nowadays the more sophisticated spellcheckers use neural networks to check that the correct homonym is used. Also, for languages with more complicated morphologies than English, spellchecking can become very computationally intensive. The technology here can perform and transform unstructured data into meaningful information. The reviews and feedback can occur from social media platforms, contact forms, direct mailing, and others.

​Answering Questions without Context

In academic circles, text summarization is used to create content abstracts. To do that, the app has to be taught to understand the accent and language patterns of a given celebrity to generate believable language. Like all GPS apps, it comes with a standard female voice that guides you as you drive. But you can also download voice packs of famous people like Arnold Schwarzenegger and Mr. T to make your drive just a bit more entertaining.

They use Natural Language Processing to make sense of these words and how they are interconnected to form different sentences. Language identification finds widespread usage in various domains, such as multilingual customer support, web content filtering, and internationalization of software applications. Named entity recognition allows identifying influential individuals or organizations within social media discussions. Moreover, sentiment analysis may be applied to understand the user’s sentiment and refine search results accordingly. By employing NLP, search engines continuously enhance their ability to understand user intent and deliver more personalized and contextually appropriate search outcomes, making the search experience more efficient and satisfying. Additionally, NLP can be used to summarize resumes of candidates who match specific roles in order to help recruiters skim through resumes faster and focus on specific requirements of the job.

Natural Language Processing Best Practices & Examples

Most higher-level NLP applications involve aspects that emulate intelligent behaviour and apparent comprehension of natural language. More broadly speaking, the technical operationalization of increasingly advanced aspects of cognitive behaviour represents one of the developmental trajectories of NLP (see trends among CoNLL shared tasks above). Neural machine translation, based on then-newly-invented sequence-to-sequence transformations, made obsolete the intermediate steps, such as word alignment, previously necessary for statistical machine translation. These artificial intelligence technologies will play a vital role in transforming from data-driven to intelligence-driven efforts as they shape and improve communication technologies in the coming years.

Of course, smaller survey companies may choose to analyze their data manually to conclude what they need to. But if you have to search through a database with millions of records, it won’t be possible manually. It makes more sense here to automate the process using an NLP-equipped tool. When suggesting keywords relevant to you, Google relies on a wealth of data that catalogs what other consumers search for when entering specific search terms. The company uses NLP to understand this data and the subtleties between different search terms. The goal of NLP systems and NLP applications is to get these definitions into a computer and then use them to form a structured, unambiguous sentence with a well-defined meaning.

Owners of larger social media accounts know how easy it is to be bombarded with hundreds of comments on a single post. It can be hard to understand the consensus and overall reaction to your posts without spending hours analyzing the comment section one by one. Smart assistants such as Google’s Alexa use voice recognition to understand everyday phrases and inquiries. On average, retailers with a semantic search bar experience a 2% cart abandonment rate, which is significantly lower than the 40% rate found on websites with a non-semantic search bar. SpaCy and Gensim are examples of code-based libraries that are simplifying the process of drawing insights from raw text. However, as you are most likely to be dealing with humans your technology needs to be speaking the same language as them.

Using sophisticated algorithms, TTS systems analyze the input text, interpret its linguistic structure, and generate corresponding speech with natural intonation and pronunciation. NLP enables TTS to handle diverse languages and accents, adapt to different contexts, and convey emotions effectively. Additionally, NLP can extract essential information, such as dates and events, from the email content to prioritize and organize messages effectively.

Top 15 Natural Language Processing Examples and Applications

Though not without its challenges, NLP is expected to continue to be an important part of both industry and everyday life. This key difference makes the addition of emotional context particularly appealing to businesses looking to create more positive customer experiences across touchpoints. NLP, for example, allows businesses to automatically classify incoming support queries using text classification and route them to the right department for assistance. This combination of AI in customer experience allows businesses to improve their customer service which, in turn, increases customer retention. Predictive text and its cousin autocorrect have evolved a lot and now we have applications like Grammarly, which rely on natural language processing and machine learning.

• Here are eight examples of applications of natural language processing which you may not know about.
• Here is where natural language processing comes in handy — particularly sentiment analysis and feedback analysis tools which scan text for positive, negative, or neutral emotions.
• The NLP practice is focused on giving computers human abilities in relation to language, like the power to understand spoken words and text.
• For instance, if a stock is receiving a lot of positive sentiment, an investor may consider buying more shares, while negative sentiment may prompt them to sell or hold off on buying.

Quora like applications use duplicate detection technology to keep the site functioning smoothly. Natural language processing techniques can be presented through the example of Mastercard chatbot. The bot was compatible when it came to comparing it with Facebook messenger but when it was more like a virtual assistant when comparing it with Uber’s bot. Also, NLP enables the computer to generate language which is close to the voice of a human.

Quora is a question and answer platform where you can find all sorts of information. Every piece of content on the site is generated by users, and people can learn from each other’s experiences and knowledge. The 1970s saw the development of a number of chatbot concepts based on sophisticated sets of hand-crafted rules for processing input information. In the late 1980s, singular value decomposition (SVD) was applied to the vector space model, leading to latent semantic analysis—an unsupervised technique for determining the relationship between words in a language.

The cool part about this project is not only about implementing NLP tools, but also you will learn how to upload this API over docker and use it as a web application. In this project, you want to create a model that predicts to classify comments into different categories. Organizations often want to ensure that conversations don’t get too negative. This project was a Kaggle challenge, where the participants had to suggest a solution for classifying toxic comments in several categories using NLP methods. To achieve this task, you will employ different NLP methods to get a deeper understanding of customer feedback and opinion. So, if you want to work in this field, you’re going to need a lot of practice.

NLP-based CACs screen can analyze and interpret unstructured healthcare data to extract features (e.g. medical facts) that support the codes assigned. NLP can be used to interpret the description of clinical trials, and check unstructured doctors’ notes and pathology reports, in order to recognize individuals who would be eligible to participate in a given clinical trial. Several retail shops use NLP-based virtual assistants in their stores to guide customers in their shopping journey. A virtual assistant can be in the form of a mobile application which the customer uses to navigate the store or a touch screen in the store which can communicate with customers via voice or text.

For processing large amounts of data, C++ and Java are often preferred because they can support more efficient code. The NLP Libraries and toolkits are generally available in Python, and for this reason by far the majority of NLP projects are developed in Python. Python’s interactive development environment makes it easy to develop and test new code.

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