13 Natural Language Processing Examples to Know

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. With social media listening, businesses can understand what their customers and others are saying about their brand or products on social media. NLP helps social media sentiment analysis to recognize and understand all types of data including text, videos, images, emojis, hashtags, etc. Through this enriched social media content processing, businesses are able to know how their customers truly feel and what their opinions are. In turn, this allows them to make improvements to their offering to serve their customers better and generate more revenue.

When a user uses a search engine to perform a specific search, the search engine uses an algorithm to not only search web content based on the keywords provided but also the intent of the searcher. 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. Sentiment analysis is an example of how natural language processing can be used to identify the subjective content of a text.

Repustate has helped organizations worldwide turn their data into actionable insights. Learn how these insights helped them increase productivity, customer loyalty, and sales revenue. In today’s hyperconnected world, our smartphones have become inseparable companions, constantly gathering and transmitting data about our whereabouts and movements. This trove of information, often referred to as mobile traffic data, holds a wealth of insights about human behaviour within cities, offering a unique perspective on urban dynamics and patterns of movement.

Top 7 Natural language processing solutions

Search is becoming more conversational as people speak commands and queries aloud in everyday language to voice search and digital assistants, expecting accurate responses in return. This disconnect between what a shopper wants and what retailers’ search engines are able to return costs companies billions of dollars annually. Many people don’t know much about this fascinating technology, and yet we all use it daily. In fact, if you are reading this, you have used NLP today without realizing it. NLP tools process data in real time, 24/7, and apply the same criteria to all your data, so you can ensure the results you receive are accurate – and not riddled with inconsistencies.

A machine-learning algorithm reads this dataset and produces a model which takes sentences as input and returns their sentiments. This kind of model, which takes sentences or documents as inputs and returns a label for that input, is called a document classification model. Document classifiers can also be used to classify documents by the topics they mention (for example, as sports, finance, politics, etc.). Natural language processing is one of the most complex fields within artificial intelligence. But, trying your hand at NLP tasks like sentiment analysis or keyword extraction needn’t be so difficult. There are many online NLP tools that make language processing accessible to everyone, allowing you to analyze large volumes of data in a very simple and intuitive way.

Alexa on the other hand is widely used in daily life helping people with different things like switching on the lights, car, geysers, and many other things. An ontology class is a natural-language program that is not a concept in the sense as humans use concepts. Custom tokenization helps identify and process the idiosyncrasies of each language so that the NLP can understand multilingual queries better. Pictured below is an example from the furniture retailer home24, showing search results for the German query “lampen” (lamp).

With greater potential in itself already, Artificial intelligence’s subset Natural language processing can derive meaning from human languages. Symbolic languages such as Wolfram Language are capable of interpreted processing of queries by sentences. Does your internal search engine understand natural language queries in every language you support? Join us as we go into detail about natural language search engines in ecommerce, including how and why to leverage natural language search and examples of ecommerce use cases in the wild.

Natural Language Understanding Examples

In this post, we will explore the various applications of NLP to your business and how you can use Akkio to perform NLP tasks without any coding or data science skills. “Dialing into quantified customer feedback could allow a business to make decisions related to marketing and improving the customer experience. It could also allow a business to better know if a recent shipment came with defective products, if the product development team hit or miss the mark on a recent feature, or if the marketing team generated a winning ad or not. In many applications, NLP software is used to interpret and understand human language, while ML is used to detect patterns and anomalies and learn from analyzing data.

“The decisions made by these systems can influence user beliefs and preferences, which in turn affect the feedback the learning system receives — thus creating a feedback loop,” researchers for Deep Mind wrote in a 2019 study. NLP can be used to generate these personalized recommendations, by analyzing customer reviews, search history (written or spoken), product descriptions, or even customer service conversations. By analyzing billions of sentences, these chains become surprisingly efficient predictors. They’re also very useful for auto correcting typos, since they can often accurately guess the intended word based on context. Akkio’s no-code AI platform lets you build and deploy a model into a chatbot easily.

Natural language understanding is a field that involves the application of artificial intelligence techniques to understand human languages. Natural language understanding aims to achieve human-like communication with computers by creating a digital system that can recognize and respond appropriately to human speech. The examples of NLP use cases in everyday lives of people also draw the limelight on language translation.

So a document with many occurrences of le and la is likely to be French, for example. 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. Ultimately, this will lead to precise and accurate process improvement.

Speech recognition, for example, has gotten very good and works almost flawlessly, but we still lack this kind of proficiency in natural language understanding. Your phone basically understands what you have said, but often can’t do anything with it because it doesn’t understand the meaning behind it. Also, some of the technologies out there only make you think they understand the meaning of a text. Getting a look at real world natural language processing examples helps build the case for utilizing new technology to improve the customer experience. It’s the social proof teams need to convince decision makers that the natural language processing (NLP) is worth the money and has the potential to bring in considerable returns.

Data analysis

This greatly reduces zero-results rates and the chance of customers bouncing. Because users more easily find what they’re searching for — and especially since you personalize their shopping experience by returning better results — there’s a higher chance of them converting. Bad search experiences are costly, not only in terms of proven monetary value, but also brand loyalty and advocacy. Over 75% of U.S. online shoppers report that an unsuccessful search resulted in a lost sale for the retail website. And 85% of global online consumers view a brand differently after an unsuccessful search.

• Predictive text uses a powerful neural network model to “learn” from the user’s behavior and suggest the next word or phrase they are likely to type.
• The data converted for the analysis procedure is taken by using different linguistics, statistical, and machine learning techniques.
• Natural language processing is one of the most complex fields within artificial intelligence.
• Only then can NLP tools transform text into something a machine can understand.
• Imagine a different user heads over to Bonobos’ website, and they search “men’s chinos on sale.” With an NLP search engine, the user is returned relevant, attractive products at a discounted price.

Infuse powerful natural language AI into commercial applications with a containerized library designed to empower IBM partners with greater flexibility. The Python programing language provides a wide range of tools and libraries for attacking specific NLP tasks. Many of these are found in the Natural Language Toolkit, or NLTK, an open source collection of libraries, programs, and education resources for building NLP programs. Natural language includes slang and idioms, not in formal writing but common in everyday conversation. Answering customer calls and directing them to the correct department or person is an everyday use case for NLUs.

Then, you can add the custom boundary function to the Language object by using the .add_pipe() method. Parsing text with this modified Language object will now treat the word after an ellipse as the start of a new sentence. For instance, you iterated over the Doc object with a list comprehension that produces a series of Token objects. On each Token object, you called the .text attribute to get the text contained within that token. From there, you can access a whole bunch of information about the processed text.

NLP is growing increasingly sophisticated, yet much work remains to be done. Current systems are prone to bias and incoherence, and occasionally behave erratically. Despite the challenges, machine learning engineers have many opportunities to apply NLP in ways that are ever more central to a functioning society.

Most of the top NLP examples revolve around ensuring seamless communication between technology and people. The answers to these questions would determine the effectiveness of NLP as a tool for innovation. Syntax and semantic analysis are two main techniques used in natural language processing.

Getting immediate feedback through online reviews helps to confirm or deny an apparent trend in the customer experience. Like we said earlier that getting insights into the users’ response to any product or service helps organizations to offer better solutions next time. 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.

One of the annoying consequences of not normalising spelling is that words like normalising/normalizing do not tend to be picked up as high frequency words if they are split between variants. For natural language example that reason we often have to use spelling and grammar normalisation tools. There has recently been a lot of hype about transformer models, which are the latest iteration of neural networks.

She has a keen interest in topics like Blockchain, NFTs, Defis, etc., and is currently working with 101 Blockchains as a content writer and customer relationship specialist. Top word cloud generation tools can transform your insight visualizations with their creativity, and give them an edge. One of the best NLP examples is found in the insurance industry where NLP is used for fraud detection. It does this by analyzing previous fraudulent claims to detect similar claims and flag them as possibly being fraudulent. This not only helps insurers eliminate fraudulent claims but also keeps insurance premiums low. Many of the unsupported languages are languages with many speakers but non-official status, such as the many spoken varieties of Arabic.

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. Wondering what are the best NLP usage examples that apply to your life?

In a 2017 paper titled “Attention is all you need,” researchers at Google introduced transformers, the foundational neural network architecture that powers GPT. Transformers revolutionized NLP by addressing the limitations of earlier models such as recurrent neural networks (RNNs) and long short-term memory (LSTM). By knowing the structure of sentences, we can start trying to understand the meaning of sentences. We start off with the meaning of words being vectors but we can also do this with whole phrases and sentences, where the meaning is also represented as vectors.

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. Auto-correct helps you find the right search keywords if you misspelt something, or used a less common name. 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. Natural Language Processing is what computers and smartphones use to understand our language, both spoken and written.

Modern deep neural network NLP models are trained from a diverse array of sources, such as all of Wikipedia and data scraped from the web. The training data might be on the order of 10 GB or more in size, and it might take a week or more on a high-performance cluster to train the deep neural network. (Researchers find that training even deeper models from even larger datasets have even higher performance, so currently there is a race to train bigger and bigger models from larger and larger datasets). Research on NLP began shortly after the invention of digital computers in the 1950s, and NLP draws on both linguistics and AI. However, the major breakthroughs of the past few years have been powered by machine learning, which is a branch of AI that develops systems that learn and generalize from data. Deep learning is a kind of machine learning that can learn very complex patterns from large datasets, which means that it is ideally suited to learning the complexities of natural language from datasets sourced from the web.

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. In general coverage is very good for major world languages, with some outliers (notably Yue and Wu Chinese, sometimes known as Cantonese and Shanghainese). The science of identifying authorship from unknown texts is called forensic stylometry. Every author has a characteristic fingerprint of their writing style – even if we are talking about word-processed documents and handwriting is not available. You would think that writing a spellchecker is as simple as assembling a list of all allowed words in a language, but the problem is far more complex than that.

Recent years have brought a revolution in the ability of computers to understand human languages, programming languages, and even biological and chemical sequences, such as DNA and protein structures, that resemble language. The latest AI models are unlocking these areas to analyze the meanings of input text and generate meaningful, expressive output. Natural language processing (NLP) is one of the most exciting aspects of machine learning and artificial intelligence. In this blog, we bring you 14 NLP examples that will help you understand the use of natural language processing and how it is beneficial to businesses. Through these examples of natural language processing, you will see how AI-enabled platforms understand data in the same manner as a human, while decoding nuances in language, semantics, and bringing insights to the forefront.

In machine translation done by deep learning algorithms, language is translated by starting with a sentence and generating vector representations that represent it. Then it starts to generate words in another language that entail the same information. Insurance companies can assess claims with natural language processing since this technology can handle both structured and unstructured data. NLP can also be trained to pick out unusual information, allowing teams to spot fraudulent claims. Watson is one of the known natural language processing examples for businesses providing companies to explore NLP and the creation of chatbots and others that can facilitate human-computer interaction. Known for offering next-generation customer service solutions, TaskUs, is the next big natural language processing example for businesses.

SaaS platforms are great alternatives to open-source libraries, since they provide ready-to-use solutions that are often easy to use, and don’t require programming or machine learning knowledge. So for machines to understand natural language, it first needs to be transformed into something that they can interpret. Once NLP tools can understand what a piece of text is about, and even measure things like sentiment, businesses can start to prioritize and organize their data in a way that suits their needs.

• Features like autocorrect, autocomplete, and predictive text are so embedded in social media platforms and applications that we often forget they exist.
• For instance, NLP is the core technology behind virtual assistants, such as the Oracle Digital Assistant (ODA), Siri, Cortana, or Alexa.
• They’re also very useful for auto correcting typos, since they can often accurately guess the intended word based on context.

These devices are trained by their owners and learn more as time progresses to provide even better and specialized assistance, much like other applications of NLP. Spam filters are where it all started – they uncovered patterns of words or phrases that were linked to spam messages. Since then, filters have been continuously upgraded to cover more use cases.

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. MarketMuse is one such natural language processing example powered by NLP and AI. The software analyzed each article written to give a direction to the writers for bringing the highest quality to each piece. A few important features of chatbots include users to navigate articles, products, services, recommendations, solutions, etc.

The process of gathering information helps organizations to gain insights into marketing campaigns along with monitoring what trends are in the market used by the customers majorly and what users are looking for. This will help in enhancing the services for better customer experience. Search engines are the next natural language processing examples that use NLP for offering better results similar to search behaviors or user intent. This will help users find things they want without being reliable to search term wizard. What comes naturally to humans is challenging for computers in terms of unstructured data, absence of real-word intent, or maybe lack of formal rules. Machine learning simplifies the extremely complex task of layering business KPIs on top of personalized search results.

In NLP, such statistical methods can be applied to solve problems such as spam detection or finding bugs in software code. Predictive typing helps you by suggesting the next word in the sentence. Creating a perfect code frame is hard, but thematic analysis software makes the process much easier.

You can foun additiona information about ai customer service and artificial intelligence and NLP. At the same time, NLP offers a promising tool for bridging communication barriers worldwide by offering language translation functions. Smart virtual assistants are the most complex examples of NLP applications in everyday life. However, the emerging trends for combining speech recognition with natural language understanding could help in creating personalized experiences for users. At the same time, NLP could offer a better and more sophisticated approach to using customer feedback surveys. The top NLP examples in the field of consumer research would point to the capabilities of NLP for faster and more accurate analysis of customer feedback to understand customer sentiments for a brand, service, or product.