13 Natural Language Processing Examples to Know

They rely on a combination of advanced NLP and natural language understanding (NLU) techniques to process the input, determine the user intent, and generate or retrieve appropriate answers. NLP has its roots in the 1950s with the development of machine translation systems. The field has since expanded, driven by advancements in linguistics, computer science, and artificial intelligence. The DataRobot AI Platform is the only complete AI lifecycle platform that interoperates with your existing investments in data, applications and business processes, and can be deployed on-prem or in any cloud environment.

It also includes libraries for implementing capabilities such as semantic reasoning, the ability to reach logical conclusions based on facts extracted from text. It’s important to assess your options based on your employee and financial resources when making the Build vs. Buy Decision for a Natural Language Processing tool. Corporations are always trying to automate repetitive tasks and focus on the service tickets that are more complicated.

Real-Life Examples of NLP in Action

Their NLP apps can process unstructured data using both linguistic and statistical algorithms. Chatbots, machine translation tools, analytics platforms, voice assistants, sentiment analysis platforms, and AI-powered transcription tools are some applications of NLG. NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly—even in real time. There’s a good chance you’ve interacted with NLP in the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences. But NLP also plays a growing role in enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processes. Businesses use massive quantities of unstructured, text-heavy data and need a way to efficiently process it.

A lot of the information created online and stored in databases is natural human language, and until recently, businesses could not effectively analyze this data. With its AI and NLP services, Maruti Techlabs allows businesses to apply personalized searches to large data sets. A suite of NLP capabilities compiles data from multiple sources and refines this data to include only useful information, relying on techniques like semantic and pragmatic analyses. In addition, artificial neural networks can automate these processes by developing advanced linguistic models.

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Chatbots are programs used to provide automated answers to common customer queries. They have pattern recognition systems with heuristic responses, which are used to hold conversations with humans. Chatbots in healthcare, for example, can collect intake data, help patients assess their symptoms, and determine next steps. These chatbots can set up appointments with the right doctor and even recommend treatments.

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.

The most direct way to manipulate a computer is through code — the computer’s language. By enabling computers to understand human language, interacting with computers becomes much more intuitive for humans. NLP can be used to great effect in a variety of business operations and processes to make them more efficient. One of the best ways to understand NLP is by looking at examples of natural language processing in practice. Similarly, support ticket routing, or making sure the right query gets to the right team, can also be automated.

POS stands for parts of speech, which includes Noun, verb, adverb, and Adjective. It indicates that how a word functions with its meaning as well as grammatically within the sentences. A word has one or more parts of speech based on the context in which it is used.

Another area where NLP is making significant headway is in the realm of digital marketing. By analyzing customer sentiment and behavior, NLP-powered marketing tools can generate insights that help marketers create more effective campaigns and personalized content. This technology can also be used to optimize search engine rankings by improving website copy and identifying high-performing keywords. Natural Language Processing (NLP) is a field of data science and artificial intelligence that studies how computers and languages interact. The goal of NLP is to program a computer to understand human speech as it is spoken. Speech recognition is an excellent example of how NLP can be used to improve the customer experience.

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Smart search is another tool that is driven by NPL, and can be integrated to ecommerce search functions. This tool learns about customer intentions with every interaction, then offers related results. IBM’s Global Adoption Index cited that almost half of businesses surveyed globally are using some kind of application powered by NLP. If you’re not adopting NLP technology, you’re probably missing out on ways to automize or gain business insights. And yet, although NLP sounds like a silver bullet that solves all, that isn’t the reality. Getting started with one process can indeed help us pave the way to structure further processes for more complex ideas with more data.

This makes for fun experiments where individuals will share entire sentences made up entirely of predictive text on their phones. The results are surprisingly personal and enlightening; they’ve even been highlighted by several media outlets. However, computers cannot interpret this data, which is in natural language, as they communicate in 1s and 0s. Hence, you need computers to be able to understand, emulate and respond intelligently to human speech. The application of semantic analysis enables machines to understand our intentions better and respond accordingly, making them smarter than ever before.

What Is Natural Language Understanding (NLU)?

Now they can focus on analyzing data to find what’s relevant amidst the chaos, and gain valuable insights that help drive the right business decisions. Natural language processing is behind the scenes for several things you may take for granted every day. When you ask Siri for directions or to send a text, natural language processing enables that functionality.

We often misunderstand one thing for another, and we often interpret the same sentences or words differently. As just one example, brand sentiment analysis is one of the top use cases for NLP in business. Many brands track sentiment on social media and perform social media sentiment analysis.

How does AI relate to natural language processing?

It also helps to quickly find relevant information from databases containing millions of documents in seconds. UniLM, or the Unified Language Model, is an advanced language model developed by Microsoft Research. What sets it apart is its ability to handle a variety of language tasks without needing specific fine-tuning for each task. This unified approach simplifies the use of NLP technology across various business applications. To launch your career in NLP, you’ll need a strong background in computer science, mathematics and linguistics.

If you consider yourself an NLP specialist, then the projects below are perfect for you. They are challenging and equally interesting projects that will allow you to further develop your NLP skills. This heading has those sample  projects on NLP that are not as effortless as the ones mentioned in the previous section. For beginners in NLP who are looking for a challenging task to test their skills, these cool NLP projects will be a good starting point. Also, you can use these NLP project ideas for your graduate class NLP projects.

Using natural language processing allows businesses to quickly analyze large amounts of data at once which makes it easier for them to gain valuable insights into what resonates most with their customers. Doing right by searchers, and ultimately your customers or buyers, requires machine learning algorithms that constantly improve and develop insights into what customers mean and want. With AI, communication becomes more human-like and contextual, allowing your brand to provide a personalized, high-quality shopping experience to each customer. This leads to increased customer satisfaction and loyalty by enabling a better understanding of preferences and sentiments. Generative AI involves using machine learning algorithms to create realistic and coherent outputs based on raw data and training data.

• These algorithms can detect changes in tone of voice or textual form when deployed for customer service applications like chatbots.
• By performing sentiment analysis, companies can better understand textual data and monitor brand and product feedback in a systematic way.
• Depending on the complexity of the chatbots, they can either just respond to specific keywords or they can even hold full conversations that make it tough to distinguish them from humans.
• When we tokenize words, an interpreter considers these input words as different words even though their underlying meaning is the same.
• With this technology at your fingertips, you can take advantage of AI capabilities while offering customers personalized experiences.

In this case, the bot is an AI hiring assistant that initializes the preliminary job interview process, matches candidates with best-fit jobs, updates candidate statuses and sends automated SMS messages to candidates. Because of this constant engagement, companies are less likely to lose well-qualified candidates due to unreturned messages and missed opportunities to fill roles that better suit certain candidates. From translation and order processing to employee recruitment and text summarization, here are more NLP examples and applications across an array of industries. Autocorrect relies on NLP and machine learning to detect errors and automatically correct them. “One of the features that use Natural Language Processing (NLP) is the Autocorrect function. This feature works on every smartphone keyboard regardless of the brand.

Sites that are specifically designed to have questions and answers for their users like Quora and Stackoverflow often request their users to submit five words along with the question so that they can be categorized easily. But, sometimes users provide wrong tags which makes it difficult for other users to navigate through. Thus, they require an automatic question tagging system that can automatically identify correct and relevant tags for a question submitted by the user. For newbies in machine learning, understanding Natural Language Processing (NLP) can be quite difficult. To smoothly understand NLP, one must try out simple projects first and gradually raise the bar of difficulty.

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