8 Natural Language Processing NLP Examples

These models are trained on large datasets and learn patterns from the data to make predictions or generate human-like responses. Popular NLP models include Recurrent Neural Networks (RNNs), Transformers, and BERT (Bidirectional Encoder Representations from Transformers). NLP powers social listening by enabling machine learning algorithms to track and identify key topics defined by marketers based on their goals. Grocery chain Casey’s used this feature in Sprout to capture their audience’s voice and use the insights to create social content that resonated with their diverse community. Many of the tools that make our lives easier today are possible thanks to natural language processing (NLP) – a subfield of artificial intelligence that helps machines understand natural human language.

NLP can be challenging to implement correctly, you can read more about that here, but when’s it’s successful it offers awesome benefits. Compared to chatbots, smart assistants in their current form are more task- and command-oriented. Too many results of little relevance is almost as unhelpful as no results at all. As a Gartner survey pointed out, workers who are unaware of important information can make the wrong decisions. To be useful, results must be meaningful, relevant and contextualized. Online search is now the primary way that people access information.

Symbolic NLP (1950s – early 1990s)

Using Waston Assistant, businesses can create natural language processing applications that can understand customer and employee languages while reverting back to a human-like conversation manner. Known for offering next-generation customer service solutions, TaskUs, is the next big natural language processing example for businesses. By using it, companies can take advantage of their automation processes for delivering solutions to customers faster. The Wonderboard mentioned earlier offers automatic insights by using natural language processing techniques.

From a broader perspective, natural language processing can work wonders by extracting comprehensive insights from unstructured data in customer interactions. The global NLP market might have a total worth of $43 billion by 2025. Artificial intelligence is no longer a fantasy element in science-fiction novels and movies. The adoption of AI through automation and conversational AI tools such as ChatGPT showcases positive emotion towards AI. Natural language processing is a crucial subdomain of AI, which wants to make machines ‘smart’ with capabilities for understanding natural language.

At the same time, NLP offers a promising tool for bridging communication barriers worldwide by offering language translation functions. Well, it allows computers to understand human language and then analyze huge amounts of language-based data in an unbiased way. In addition to that, there are thousands of human languages in hundreds of dialects that are spoken in different ways by different ways.

In other words, the search engine “understands” what the user is looking for. For example, if a user searches for “apple pricing” the search will return results based on the current prices of Apple computers and not those of the fruit. NLP combines rule-based modeling of human language called computational linguistics, with other models such as statistical models, Machine Learning, and deep learning. When integrated, these technological models allow computers to process human language through either text or spoken words. As a result, they can ‘understand’ the full meaning – including the speaker’s or writer’s intention and feelings.

While tokenizing allows you to identify words and sentences, chunking allows you to identify phrases. Part of speech is a grammatical term that deals with the roles words play when you use them together in sentences. Tagging parts of speech, or POS tagging, is the task of labeling the words in your text according to their part of speech.

People go to social media to communicate, be it to read and listen or to speak and be heard. As a company or brand you can learn a lot about how your customer feels by what they comment, post about or listen to. Coreference resolution is one of the most difficult steps in our pipeline to natural language programming examples implement. Recent advances in deep learning have resulted in new approaches that are more accurate, but it isn’t perfect yet. These are shortcuts that we use instead of writing out names over and over in each sentence. Humans can keep track of what these words represent based on context.

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. Data analysis has come a long way in interpreting survey results, although the final challenge is making sense of open-ended responses and unstructured text. NLP, with the support of other AI disciplines, is working towards making these advanced analyses possible. Companies nowadays have to process a lot of data and unstructured text.

Predictive typing helps you by suggesting the next word in the sentence. This week I am in Singapore, speaking on the topic of Natural Language Processing (NLP) at the Strata conference. If you haven’t heard of NLP, or don’t quite understand what it is, you are not alone. Many people don’t know much about this fascinating technology and yet use it every day.

Deliver exceptional frontline agent experiences to improve employee productivity and engagement, as well as improved customer experience. Natural Language Processing is what computers and smartphones use to understand our language, both spoken and written. Because we use language to interact with our devices, NLP became an integral part of our lives.

Natural Language Processing is a cross among many different fields such as artificial intelligence, computational linguistics, human-computer interaction, etc. There are many different methods in NLP to understand human language which include statistical and machine learning methods. These involve breaking down human language into its most basic pieces and then understand how these pieces relate to each other and work together to create meanings in sentences. One of the most challenging and revolutionary things artificial intelligence (AI) can do is speak, write, listen, and understand human language. Natural language processing (NLP) is a form of AI that extracts meaning from human language to make decisions based on the information. This technology is still evolving, but there are already many incredible ways natural language processing is used today.

Today, deep learning has changed the landscape of NLP, enabling computers to perform tasks that would have been thought impossible a decade ago. Deep learning has enabled deep neural networks to peer inside images, describe their scenes, and provide overviews of videos. TensorFlow, along with its high-level API Keras, is a popular deep learning framework used for NLP.

Find Top NLP Talent!

NLP will continue to be an important part of both industry and everyday life. Syntax and semantic analysis are two main techniques used in natural language processing. The working mechanism in most of the NLP examples focuses on visualizing a sentence as a ‘bag-of-words’. NLP ignores the order of appearance of words in a sentence and only looks for the presence or absence of words in a sentence. The ‘bag-of-words’ algorithm involves encoding a sentence into numerical vectors suitable for sentiment analysis.

NLP is used in many other areas such as social media monitoring, translation tools, smart home devices, survey analytics, etc. Chances are you may have used Natural Language Processing a lot of times till now but never realized what it was. But now you know the insane amount of applications of this technology and how it’s improving our daily lives. If you want to learn more about this technology, there are various online courses you can refer to.

Harmony reaches final of Wellcome Trust Data Prize

Combining AI, machine learning and natural language processing, Covera Health is on a mission to raise the quality of healthcare with its clinical intelligence platform. The company’s platform links to the rest of an organization’s infrastructure, streamlining operations and patient care. Once professionals have adopted Covera Health’s platform, it can quickly scan images without skipping over important details and abnormalities. Healthcare workers no longer have to choose between speed and in-depth analyses.

Google is one of the best examples of using NLP in predictive text analysis. Predictive text analysis applications utilize a powerful neural network model for learning from the user behavior to predict the next phrase or word. On top of it, the model could also offer suggestions for correcting the words and also help in learning new words. Most important of all, the personalization aspect of NLP would make it an integral part of our lives.

Natural Language Processing plays a vital role in grammar checking software and auto-correct functions. Tools like Grammarly, for example, use NLP to help you improve your writing, by detecting grammar, spelling, or sentence structure errors. You can foun additiona information about ai customer service and artificial intelligence and NLP. You could pull out the information you need and set up a trigger to automatically enter this information in your database.

You might be able to save a lot of time by applying NLP techniques to your own projects. Computers are great at working with structured data like spreadsheets and database tables. NLP gives computers the ability to understand spoken words and text the same as humans do. It divides the entire paragraph into different sentences for better understanding. Most higher-level NLP applications involve aspects that emulate intelligent behaviour and apparent comprehension of natural language.

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. Teams can then organize extensive data sets at a rapid pace and extract essential insights through NLP-driven searches.

Key topic modelling algorithms include k-means and Latent Dirichlet Allocation. You can read more about k-means and Latent Dirichlet Allocation in my review of the 26 most important data science concepts. But a lot of the data floating around companies is in an unstructured format such as PDF documents, and this is where Power BI cannot help so easily.

Similarly, support ticket routing, or making sure the right query gets to the right team, can also be automated. This is done by using NLP to understand what the customer needs based on the language they are using. This is then combined with deep learning technology to execute the routing. One of the tell-tale signs of cheating on your Spanish homework is that grammatically, it’s a mess.

Words that appear more frequently in the sentence will have a higher numerical value than those that appear less often, and words like “the” or “a” that do not indicate sentiment are ignored. Auto-correct helps you find the right search keywords if you misspelt something, or used a less common name. “According to research, making a poor hiring decision based on unconscious prejudices can cost a company up to 75% of that person’s annual income. Conversation analytics makes it possible to understand and serve insurance customers by mining 100% of contact center interactions.

Going Deeper

Here are eight examples of applications of natural language processing which you may not know about. If you have a large amount of text data, don’t hesitate to hire an NLP consultant such as Fast Data Science. Poor search function is a surefire way to boost your bounce rate, which is why self-learning search is a must for major e-commerce players. Several prominent clothing retailers, including Neiman Marcus, Forever 21 and Carhartt, incorporate BloomReach’s flagship product, BloomReach Experience (brX). The suite includes a self-learning search and optimizable browsing functions and landing pages, all of which are driven by natural language processing. Translation company Welocalize customizes Googles AutoML Translate to make sure client content isn’t lost in translation.

NLP is used for a wide variety of language-related tasks, including answering questions, classifying text in a variety of ways, and conversing with users. The ability to mine these data to retrieve information or run searches is important. The HMM was also applied to problems in NLP, such as part-of-speech taggingOpens a new window (POS). POS tagging, as the name implies, tags the words in a sentence with its part of speech (noun, verb, adverb, etc.).

Grammerly used this capability to gain industry and competitive insights from their social listening data. They were able to pull specific customer feedback from the Sprout Smart Inbox to get an in-depth view of their product, brand health and competitors. Social listening provides a wealth of data you can harness to get up close and personal with your target audience. However, qualitative data can be difficult to quantify and discern contextually. NLP overcomes this hurdle by digging into social media conversations and feedback loops to quantify audience opinions and give you data-driven insights that can have a huge impact on your business strategies. Many companies are using social media to enhance their brand awareness.

This trend is not foreign to AI research, which has seen many AI springs and winters in which significant interest was generated only to lead to disappointment and failed promises. The allure of NLP, given its importance, nevertheless meant that research continued to break free of hard-coded rules and into the current state-of-the-art connectionist models. In the mid-1950s, IBM sparked tremendous excitement for language understanding through the Georgetown experiment, a joint development project between IBM and Georgetown University. NLP equipped Wonderflow’s Wonderboard brings customer feedback and then analyzes them. By using NLP technology, a business can improve its content marketing strategy.

It is used to derive intelligence from unstructured data for purposes such as customer experience analysis, brand intelligence and social sentiment analysis. We don’t regularly think about the intricacies of our own languages. 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).

It plays a role in chatbots, voice assistants, text-based scanning programs, translation applications and enterprise software that aids in business operations, increases productivity and simplifies different processes. By capturing the unique complexity of unstructured language data, AI and natural language understanding technologies empower NLP systems to understand the context, meaning and relationships present in any text. This helps search systems understand the intent of users searching for information and ensures that the information being searched for is delivered in response. Natural language processing is used when we want machines to interpret human language.

ChatGPT is one of the best natural language processing examples with the transformer model architecture. Transformers follow a sequence-to-sequence deep learning architecture that takes user inputs in natural language and generates output in natural language according to its training data. As mentioned earlier, virtual assistants use natural language generation to give users their desired response. To note, another one of the great examples of natural language processing is GPT-3 which can produce human-like text on almost any topic. The model was trained on a massive dataset and has over 175 billion learning parameters. As a result, it can produce articles, poetry, news reports, and other stories convincingly enough to seem like a human writer created them.

And even better, the latest advances in NLP are easily accessible through open source Python libraries like spaCy, textacy, and neuralcoref. Next comes dependency parsing which is mainly used to find out how all the words in a sentence are related to each other. To find the dependency, we can build a tree and assign a single word as a parent word. The next step is to consider the importance of each and every word in a given sentence. In English, some words appear more frequently than others such as “is”, “a”, “the”, “and”.

Quora like applications use duplicate detection technology to keep the site functioning smoothly. The MasterCard virtual assistant chatbot can provide a 360 eagle view of the user spending habits along with offering them what benefits they can take from the card. Chatbots are the most integral part of any mobile app or a website and integrating NLP into them can increase the usefulness. The role of chatbots in enterprise along with NLP lessens the need to enroll more staff for every customer.

• These NLP applications are helping humans to perform daily tasks such as sending messages, language translation, and many more.
• This technology even extends to languages like Russian and Chinese, which are traditionally more difficult to translate due to their different alphabet structure and use of characters instead of letters.
• Let’s take the idea of detecting entities and twist it around to build a data scrubber.
• The ability to mine these data to retrieve information or run searches is important.
• The role of chatbots in enterprise along with NLP lessens the need to enroll more staff for every customer.

A natural-language program is a precise formal description of some procedure that its author created. It is human readable and it can also be read by a suitable software agent. For example, a web page in an NLP format can be read by a software personal assistant agent to a person and she or he can ask the agent to execute some sentences, i.e. carry out some task or answer a question. There is a reader agent available for English interpretation of HTML based NLP documents that a person can run on her personal computer .

As a result, many businesses now look to NLP and text analytics to help them turn their unstructured data into insights. Core NLP features, such as named entity extraction, give users the power to identify key elements like names, dates, currency values, and even phone numbers in text. Sentiment Analysis is also widely used on Social Listening processes, on platforms such as Twitter.

NLP works through normalization of user statements by accounting for syntax and grammar, followed by leveraging tokenization for breaking down a statement into distinct components. Finally, the machine analyzes the components and draws the meaning of the statement by using different algorithms. We are very satisfied with the accuracy of Repustate’s Arabic sentiment analysis, as well as their and support which helped us to successfully deliver the requirements of our clients in the government and private sector. The use of NLP in the insurance industry allows companies to leverage text analytics and NLP for informed decision-making for critical claims and risk management processes. Now, thanks to AI and NLP, algorithms can be trained on text in different languages, making it possible to produce the equivalent meaning in another language.

These are the 12 most prominent natural language processing examples and there are many in the lines used in the healthcare domain, for aircraft maintenance, for trading, and a lot more. Automatic insights not just focuses on analyzing or identifying the trends but generate insights about the service or product performance in a sentence form. This helps in developing the latest version of the product or expanding the services. By collecting the plus and minus based on the reviews, it helps companies to gain insight of products’ or services’ best qualities and the features most liked/disliked by the users. Through social media reviews, ratings, and feedback, it becomes easier for organizations to offer results users are asking for.