8 Real-World Examples of Natural Language Processing NLP

Natural language processing could help in converting text into numerical vectors and use them in machine learning models for uncovering hidden insights. The review of best NLP examples is a necessity for every beginner who has doubts about natural language processing. Anyone learning about NLP for the first time would have questions regarding the practical implementation of NLP in the real world. On paper, the concept of machines interacting semantically with humans is a massive leap forward in the domain of technology. Another one of the crucial NLP examples for businesses is the ability to automate critical customer care processes and eliminate many manual tasks that save customer support agents’ time and allow them to focus on more pressing issues. NLP, for example, allows businesses to automatically classify incoming support queries using text classification and route them to the right department for assistance.

NLP gives computers the ability to understand spoken words and text the same as humans do. The model analyzes the parts of speech to figure out what exactly the sentence is talking about. Despite these uncertainties, it is evident that we are entering a symbiotic era between humans and machines.

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However even after the PDF-to-text conversion, the text is often messy, with page numbers and headers mixed into the document, and formatting information lost. Train, validate, tune and deploy generative AI, foundation models and machine learning capabilities with IBM watsonx.ai, a next generation enterprise studio for AI builders. The main benefit of NLP is that it improves the way humans and computers communicate with each other. The most direct way to manipulate a computer is through code — the computer’s language. Enabling computers to understand human language makes interacting with computers much more intuitive for humans. Still, as we’ve seen in many NLP examples, it is a very useful technology that can significantly improve business processes – from customer service to eCommerce search results.

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. Spam detection removes pages that match search keywords but do not provide the actual search answers.

How to use natural language in a sentence

NLP can also scan patient documents to identify patients who would be best suited for certain clinical trials. NLP-powered apps can check for spelling errors, highlight unnecessary or misapplied grammar and even suggest simpler ways to organize sentences. Natural language processing can also translate text into other languages, aiding students in learning a new language. Natural language processing can help customers book tickets, track orders and even recommend similar products on e-commerce websites.

The misspelled word is then added to a Machine Learning algorithm that conducts calculations and adds, removes, or replaces letters from the word, before matching it to a word that fits the overall sentence meaning. Then, the user has the option to correct the word automatically, or manually through spell check. You can foun additiona information about ai customer service and artificial intelligence and NLP. Sentiment analysis (also known as opinion mining) is an NLP strategy that can determine whether the meaning behind data is positive, negative, or neutral. For instance, if an unhappy client sends an email which mentions the terms “error” and “not worth the price”, then their opinion would be automatically tagged as one with negative sentiment.

Businesses use NLP to power a growing number of applications, both internal — like detecting insurance fraud, determining customer sentiment, and optimizing aircraft maintenance — and customer-facing, like Google Translate. Deep-learning models take as input a word embedding and, at each time state, return the probability distribution of the next word as the probability for every word in the dictionary. Pre-trained language models learn the structure of a particular language by processing a large corpus, such as Wikipedia. For instance, BERT has been fine-tuned for tasks ranging from fact-checking to writing headlines.

NLP enables question-answering (QA) models in a computer to understand and respond to questions in natural language using a conversational style. QA systems process data to locate relevant information and provide accurate answers. Semantic search enables a computer to contextually interpret the intention of the user without depending on keywords. These algorithms work together with NER, NNs and knowledge graphs to provide remarkably accurate results. Semantic search powers applications such as search engines, smartphones and social intelligence tools like Sprout Social. NLP powers AI tools through topic clustering and sentiment analysis, enabling marketers to extract brand insights from social listening, reviews, surveys and other customer data for strategic decision-making.

Topic classification consists of identifying the main themes or topics within a text and assigning predefined tags. For training your topic classifier, you’ll need to be familiar with the data you’re analyzing, so you can define relevant categories. 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.

Which are the top NLP techniques?

It can sort through large amounts of unstructured data to give you insights within seconds. 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. The first and most important ingredient required for natural language processing to be effective is data. Once businesses have effective data collection and organization protocols in place, they are just one step away from realizing the capabilities of NLP.

There are four stages included in the life cycle of NLP – development, validation, deployment, and monitoring of the models. Python is considered the best programming language for NLP because of their numerous libraries, simple syntax, and ability to easily integrate with other programming languages. Named entity recognition (NER) concentrates on determining which items in a text (i.e. the “named entities”) can be located and classified into predefined categories. These categories can range from the names of persons, organizations and locations to monetary values and percentages. For example, the stem for the word “touched” is “touch.” “Touch” is also the stem of “touching,” and so on.

• The field has since expanded, driven by advancements in linguistics, computer science, and artificial intelligence.
• NLP can also analyze customer surveys and feedback, allowing teams to gather timely intel on how customers feel about a brand and steps they can take to improve customer sentiment.
• This involves generating synopses of large volumes of text by extracting the most critical and relevant information.
• You can then be notified of any issues they are facing and deal with them as quickly they crop up.
• 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.

We can suppose that each English sentence represents a distinct thinking or idea. Writing a program to understand a single sentence will be far easier than understanding a whole paragraph. Splitting sentences apart anytime you see a punctuation mark is a straightforward way to code a Sentence Segmentation model. Modern NLP pipelines, on the other hand, frequently employ more advanced algorithms that operate even when a page isn’t well-formatted. Natural language understanding is the process of identifying the meaning of a text, and it’s becoming more and more critical in business. Natural language understanding software can help you gain a competitive advantage by providing insights into your data that you never had access to before.

Natural Language Processing (NLP) is a subfield of AI that focuses on the interaction between computers and humans through natural language. The main goal of NLP is to enable computers to understand, interpret, and generate human language in a way that is both meaningful and useful. NLP plays an essential role in many applications you use daily—from search engines and chatbots, to voice assistants and sentiment analysis. NLP drives automatic machine translations of text or speech data from one language to another. NLP uses many ML tasks such as word embeddings and tokenization to capture the semantic relationships between words and help translation algorithms understand the meaning of words.

For example, businesses can recognize bad sentiment about their brand and implement countermeasures before the issue spreads out of control. One problem I encounter again and again is running natural language processing algorithms on documents corpora or lists of survey responses which are a mixture of American and British spelling, or full of common spelling mistakes. 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.

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. Smart assistants such as Google’s Alexa use voice recognition to understand everyday phrases and inquiries. Spellcheck is one of many, and it is so common today that it’s often taken for granted. This feature essentially notifies the user of any spelling errors they have made, for example, when setting a delivery address for an online order. 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.

They employ a mechanism called self-attention, which allows them to process and understand the relationships between words in a sentence—regardless of their positions. This self-attention mechanism, combined with the parallel processing capabilities of transformers, helps them achieve more efficient and accurate language modeling than their predecessors. One computer in 2014 did convincingly pass the test—a chatbot with the persona of a 13-year-old boy. This is not to say that an intelligent machine is impossible to build, but it does outline the difficulties inherent in making a computer think or converse like a human. Natural language processing (NLP) is of critical importance because it helps structure this unstructured data and reduce the ambiguity in natural language.

natural language processing (NLP)

In the healthcare industry, machine translation can help quickly process and analyze clinical reports, patient records, and other medical data. This can dramatically improve the customer experience and provide a better understanding of patient health. Bag-of-words, for example, is an algorithm that encodes a sentence into a numerical vector, which can be used for sentiment analysis. The effective classification of customer sentiments about products and services of a brand could help companies in modifying their marketing strategies.

Voice recognition, or speech-to-text, converts spoken language into written text; speech synthesis, or text-to-speech, does the reverse. These technologies enable hands-free interaction with devices and improved accessibility for individuals with disabilities. A majority of today’s software applications employ NLP techniques to assist you in accomplishing tasks. It’s highly likely that you engage with NLP-driven technologies on a daily basis. NLP attempts to make computers intelligent by making humans believe they are interacting with another human. The Turing test, proposed by Alan Turing in 1950, states that a computer can be fully intelligent if it can think and make a conversation like a human without the human knowing that they are actually conversing with a machine.

Learn more about NLP fundamentals and find out how it can be a major tool for businesses and individual users. The outline of natural language processing examples must emphasize the possibility of using NLP for generating personalized recommendations for e-commerce. NLP models could analyze customer reviews and search history of customers through text and voice data alongside customer service conversations and product descriptions. It is important to note that other complex domains of NLP, such as Natural Language Generation, leverage advanced techniques, such as transformer models, for language processing. ChatGPT is one of the best natural language processing examples with the transformer model architecture.

Some of these tasks have direct real-world applications, while others more commonly serve as subtasks that are used to aid in solving larger tasks. NLP is a branch of Artificial Intelligence that deals with understanding and generating natural language. It allows computers to understand the meaning of words and phrases, as well as the context in which they’re used. Most important of all, the personalization aspect of NLP would make it an integral part of our lives.

The field of NLP has been around for decades, but recent advances in machine learning have enabled it to become increasingly powerful and effective. Companies are now able to analyze vast amounts of customer data and extract insights from it. This can be used for a variety of use-cases, including customer segmentation and marketing personalization. Just like any new technology, it is difficult to measure the potential of NLP for good without exploring its uses. Most important of all, you should check how natural language processing comes into play in the everyday lives of people.

The review of top NLP examples shows that natural language processing has become an integral part of our lives. It defines the ways in which we type inputs on smartphones and also reviews our opinions about products, services, and brands on social media. At the same time, NLP offers a promising tool for bridging communication barriers worldwide by offering language translation functions. There has recently been a lot of hype about transformer models, which are the latest iteration of neural networks.

The different examples of natural language processing in everyday lives of people also include smart virtual assistants. You can notice that smart assistants such as Google Assistant, Siri, and Alexa have gained formidable improvements in popularity. The voice assistants are the best NLP examples, which work through speech-to-text conversion and intent classification for classifying inputs as action or question. Smart virtual assistants could also track and remember important user information, such as daily activities. Natural language processing (NLP) is the science of getting computers to talk, or interact with humans in human language. Examples of natural language processing include speech recognition, spell check, autocomplete, chatbots, and search engines.

Stemming reduces words to their root or base form, eliminating variations caused by inflections. For example, the words “walking” and “walked” share the root “walk.” In our example, the stemmed form of “walking” would be “walk.” This involves generating synopses of large volumes of text by extracting the most critical and relevant information. The goal is to create a tree that gives each word in the text a single parent word.

Interestingly, the Bible has been translated into more than 6,000 languages and is often the first book published in a new language. Many of the unsupported languages are languages with many speakers but non-official status, such as the many spoken varieties of Arabic. By counting the one-, two- and three-letter sequences in a text (unigrams, bigrams and trigrams), a language can be identified from a short sequence of a few sentences only. A slightly more sophisticated technique for language identification is to assemble a list of N-grams, which are sequences of characters which have a characteristic frequency in each language.

Natural language processing can be used to improve customer experience in the form of chatbots and systems for triaging incoming sales enquiries and customer support requests. Natural language processing has been around for years but is often taken for granted. Here are eight examples of applications natural language examples 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. Many companies have more data than they know what to do with, making it challenging to obtain meaningful insights.

From a broader perspective, natural language processing can work wonders by extracting comprehensive insights from unstructured data in customer interactions. The monolingual based approach is also far more scalable, as Facebook’s models are able to translate from Thai to Lao or Nepali to Assamese as easily as they would translate between those languages and English. As the number of supported languages increases, the number of language pairs would become unmanageable if each language pair had to be developed and maintained. Earlier iterations of machine translation models tended to underperform when not translating to or from English. I often work using an open source library such as Apache Tika, which is able to convert PDF documents into plain text, and then train natural language processing models on the plain text.

Also, for languages with more complicated morphologies than English, spellchecking can become very computationally intensive. It also includes libraries for implementing capabilities such as semantic reasoning, the ability to reach logical conclusions based on facts extracted from text. The all new enterprise studio that brings together traditional machine learning along with new generative AI capabilities powered by foundation models.

Likewise, NLP is useful for the same reasons as when a person interacts with a generative AI chatbot or AI voice assistant. Instead of needing to use specific predefined language, a user could interact with a voice assistant like Siri on their phone using their regular diction, and their voice assistant will still be able to understand them. If you’re interested in learning more about how NLP and other AI disciplines support businesses, take a look at our dedicated use cases resource page. Regardless of the data volume tackled every day, any business owner can leverage NLP to improve their processes. To better understand the applications of this technology for businesses, let’s look at an NLP example.

Moreover, sophisticated language models can be used to generate disinformation. A broader concern is that training large models produces substantial greenhouse gas emissions. Natural language understanding is how a computer program can intelligently understand, interpret, and respond to human speech. Natural language generation is the process by which a computer program creates content based on human speech input. Companies can also use natural language understanding software in marketing campaigns by targeting specific groups of people with different messages based on what they’re already interested in. Using a natural language understanding software will allow you to see patterns in your customer’s behavior and better decide what products to offer them in the future.

Relationship extraction takes the named entities of NER and tries to identify the semantic relationships between them. This could mean, for example, finding out who is married to whom, that a person works for a specific company and so on. This problem can also be transformed into a classification problem and a machine learning model can be trained for every relationship type. Syntactic analysis (syntax) and semantic analysis (semantic) are the two primary techniques that lead to the understanding of natural language.

Milestones like Noam Chomsky’s transformational grammar theory, the invention of rule-based systems, and the rise of statistical and neural approaches, such as deep learning, have all contributed to the current state of NLP. Gathering market intelligence becomes much easier with natural language processing, which can analyze online reviews, social media posts and web forums. Compiling this data can help marketing teams understand what consumers care about and how they perceive a business’ brand. While NLP-powered chatbots and callbots are most common in customer service contexts, companies have also relied on natural language processing to power virtual assistants. These assistants are a form of conversational AI that can carry on more sophisticated discussions.

Through projects like the Microsoft Cognitive Toolkit, Microsoft has continued to enhance its NLP-based translation services. Read on to learn what natural language processing is, how NLP can make businesses more effective, and discover popular natural language processing techniques and examples. Challenges in natural language processing frequently involve speech recognition, natural-language understanding, and natural-language generation.