14 Natural Language Processing Examples NLP Examples

Natural Language Processing, or NLP, is a subfield of artificial intelligence that focuses on the interaction between computers and humans through language. It allows machines to understand, interpret, and generate human language in a valuable and meaningful way. NLP involves several complex tasks including speech recognition, natural language understanding, and natural language generation.

Although topic modeling isn’t directly applicable to our example sentence, it is an essential technique for analyzing larger text corpora. Sentiment analysis determines the sentiment or emotion expressed in a text, such as positive, negative, or neutral. While our example sentence doesn’t express a clear sentiment, this technique is widely used for brand monitoring, product reviews, and social media analysis. Here at Thematic, we use NLP to help customers identify recurring patterns in their client feedback data. We also score how positively or negatively customers feel, and surface ways to improve their overall experience. 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.

Deep learning or deep neural networks is a branch of machine learning that simulates the way human brains work. 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. 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. Natural language processing (NLP) is a subfield of artificial intelligence (AI) focused on the interaction between computers and human language.

NLP’s top applications

Oftentimes, when businesses need help understanding their customer needs, they turn to sentiment analysis. There are many eCommerce websites and online retailers that leverage NLP-powered semantic search engines. They aim to understand the shopper’s intent when searching for long-tail keywords (e.g. women’s straight leg denim size 4) and improve product visibility. None of this would be possible without NLP which allows chatbots to listen to what customers are telling them and provide an appropriate response. This response is further enhanced when sentiment analysis and intent classification tools are used.

• Understand voice and text conversations to uncover the insights needed to improve compliance and reduce risk.
• But, trying your hand at NLP tasks like sentiment analysis or keyword extraction needn’t be so difficult.
• In order for the parsing algorithm to construct this parse tree, a set of rewrite rules, which describe what tree structures are legal, need to be constructed.
• “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.

They integrate with Slack, Microsoft Messenger, and other chat programs where they read the language you use, then turn on when you type in a trigger phrase. Voice assistants such as Siri and Alexa also kick into gear when they hear phrases like “Hey, Alexa.” That’s why critics say these programs are always listening; if they weren’t, they’d never know when you need them. Unless you turn an app on manually, NLP programs must operate in the background, waiting for that phrase.

As a Gartner survey pointed out, workers who are unaware of important information can make the wrong decisions. 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. To better understand the applications of this technology for businesses, let’s look at an NLP example. NPL cross-checks text to a list of words in the dictionary (used as a training set) and then identifies any spelling errors. 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.

Moreover, sophisticated language models can be used to generate disinformation. A broader concern is that training large models produces substantial greenhouse gas emissions. NLP is one of the fast-growing research domains in AI, with applications that involve tasks including translation, summarization, text generation, and sentiment analysis.

NLP is used for other types of information retrieval systems, similar to search engines. “An information retrieval system searches a collection of natural language documents with the goal of retrieving exactly the set of documents that matches a user’s question. 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. With an ever-growing number of use cases, NLP, ML and AI are ubiquitous in modern life, and most people have encountered these technologies in action without even being aware of it. NLP can be used to great effect in a variety of business operations and processes to make them more efficient.

Siri, Alexa, or Google Assistant?

Examples include novels written under a pseudonym, such as JK Rowling’s detective series written under the pen-name Robert Galbraith, or the pseudonymous Italian author Elena Ferrante. The easiest way to get started with BERT is to install a library called Hugging Face. Below you can see my experiment retrieving the facts of the Donoghue v Stevenson (“snail in a bottle”) case, which was a landmark decision in English tort law which laid the foundation for the modern doctrine of negligence. You can see that BERT was quite easily able to retrieve the facts (On August 26th, 1928, the Appellant drank a bottle of ginger beer, manufactured by the Respondent…). Although impressive, at present the sophistication of BERT is limited to finding the relevant passage of text. Call center representatives must go above and beyond to ensure customer satisfaction.

(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). Stop words are commonly used in a language without significant meaning and are often filtered out during text preprocessing. Removing stop words can reduce noise in the data and improve the efficiency of downstream NLP tasks like text classification or sentiment analysis. 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.

This application of NLP has substantial implications in areas such as travel, international business, and cross-cultural research, where language translation is vital. A subfield of NLP called natural language understanding (NLU) has begun to rise in popularity because of its potential in cognitive and AI applications. NLU goes beyond the structural understanding of language to interpret intent, resolve context and word ambiguity, and even generate well-formed human language on its own. Other examples of machines using NLP are voice-operated GPS systems, customer service chatbots, and language translation programs. In addition, businesses use NLP to enhance understanding of and service to consumers by auto-completing search queries and monitoring social media.

The tools will notify you of any patterns and trends, for example, a glowing review, which would be a positive sentiment that can be used as a customer testimonial. 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.

NLP is special in that it has the capability to make sense of these reams of unstructured information. Tools like keyword extractors, sentiment analysis, and intent classifiers, to name a few, are particularly useful. Using NLP, more specifically sentiment analysis tools like MonkeyLearn, to keep an eye on how customers are feeling. You can then be notified of any issues they are facing and deal with them as quickly they crop up.

Auto-correct helps you find the right search keywords if you misspelt something, or used a less common name. Infuse powerful natural language AI into commercial applications with a containerized library designed to empower IBM partners with greater flexibility. Since you don’t need to create a list of predefined tags or tag any data, it’s a good option for exploratory analysis, when you are not yet familiar with your data.

With the increasing volume of text data generated every day, from social media posts to research articles, NLP has become an essential tool for extracting valuable insights and automating various tasks. CallMiner is the global leader in conversation analytics to drive business performance improvement. By connecting the dots between insights and action, CallMiner enables companies to identify areas of opportunity to drive business improvement, growth and transformational change more effectively than ever before. CallMiner is trusted by the world’s leading organizations across retail, financial services, healthcare and insurance, travel and hospitality, and more.

These systems use NLP to understand the command, extract the necessary information, and execute the action, making technology more interactive and user-friendly. Furthermore, smart assistants can also engage in two-way communication, providing responses to user inquiries in a conversational manner. This capability to understand, respond to, and learn from human language is made possible by the integration of NLP, solidifying its role in enhancing human-computer interaction. The evolution of NLP toward NLU has a lot of important implications for businesses and consumers alike. Imagine the power of an algorithm that can understand the meaning and nuance of human language in many contexts, from medicine to law to the classroom. As the volumes of unstructured information continue to grow exponentially, we will benefit from computers’ tireless ability to help us make sense of it all.

The information that populates an average Google search results page has been labeled—this helps make it findable by search engines. However, the text documents, reports, PDFs and intranet pages that make up enterprise content are unstructured data, and, importantly, not labeled. This makes it difficult, if not impossible, for the information to be retrieved by search. At the intersection of these two phenomena lies natural language processing (NLP)—the process of breaking down language into a format that is understandable and useful for both computers and humans. Optical Character Recognition (OCR) automates data extraction from text, either from a scanned document or image file to a machine-readable text. For example, an application that allows you to scan a paper copy and turns this into a PDF document.

For example, any company that collects customer feedback in free-form as complaints, social media posts or survey results like NPS, can use NLP to find actionable insights in this data. 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.

While LLMs have made strides in addressing this issue, they can still struggle with understanding subtle nuances—such as sarcasm, idiomatic expressions, or context-dependent meanings—leading to incorrect or nonsensical responses. 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. Lemmatization, similar to stemming, considers the context and morphological structure of a word to determine its base form, or lemma. It provides more accurate results than stemming, as it accounts for language irregularities.

With its focus on user-generated content, Roblox provides a platform for millions of users to connect, share and immerse themselves in 3D gaming experiences. The company uses NLP to build models that help improve the quality of text, voice and image translations so gamers can interact without language barriers. Because of their complexity, generally it takes a lot of data to train a deep neural network, and processing it takes a lot of compute power and time. 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.

With automatic summarization, NLP algorithms can summarize the most relevant information from content and create a new, shorter version of the original content. It can do this either by extracting the information and then creating a summary or it can use deep learning techniques to extract the information, paraphrase it and produce a unique natural language processing examples version of the original content. Automatic summarization is a lifesaver in scientific research papers, aerospace and missile maintenance works, and other high-efficiency dependent industries that are also high-risk. Many companies have more data than they know what to do with, making it challenging to obtain meaningful insights.

Most NLP systems are developed and trained on English data, which limits their effectiveness in other languages and cultures. Developing NLP systems that can handle the diversity of human languages and cultural nuances remains a challenge due to data scarcity for under-represented classes. However, GPT-4 has showcased significant improvements in multilingual support. Deep semantic understanding remains a challenge in NLP, as it requires not just the recognition of words and their relationships, but also the comprehension of underlying concepts, implicit information, and real-world knowledge.

In general terms, NLP tasks break down language into shorter, elemental pieces, try to understand relationships between the pieces and explore how the pieces work together to create meaning. Not only are there hundreds of languages and dialects, but within each language is a unique set of grammar and syntax rules, terms and slang. When we write, we often misspell or abbreviate words, or omit punctuation. When we speak, we have regional accents, and we mumble, stutter and borrow terms from other languages. But a computer’s native language – known as machine code or machine language – is largely incomprehensible to most people.

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. Conversation analytics can help energy and utilities companies enhance customer experience and remain compliant to industry regulations. Increase revenue while supporting customers in the tightly monitored and high-risk collections industry with conversation analytics. Delivering the best customer experience and staying compliant with financial industry regulations can be driven through conversation analytics.

A major benefit of chatbots is that they can provide this service to consumers at all times of the day. Chatbots are common on so many business websites because they are autonomous and the data they store can be used for improving customer service, managing customer complaints, improving efficiencies, product research and so much more. They can also be used for providing personalized product recommendations, offering discounts, helping with refunds and return procedures, and many other tasks.

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. Now, however, it can translate grammatically complex sentences without any problems. Deep learning is a subfield of machine learning, which helps to decipher the user’s intent, words and sentences. For example, a user can ask Siri about the weather, command Alexa to play a song, or instruct Google Assistant to set an alarm, all with their voice.

“NLP in customer service tools can be used as a first point of engagement to answer basic questions about products and features, such as dimensions or product availability, and even recommend similar products. This frees up human employees from routine first-tier requests, enabling them to handle escalated customer issues, which require more time and expertise. “Question Answering (QA) is a research area that combines research https://chat.openai.com/ from different fields, with a common subject, which are Information Retrieval (IR), Information Extraction (IE) and Natural Language Processing (NLP). Actually, current search engine just do ‘document retrieval’, i.e. given some keywords it only returns the relevant ranked documents that contain these keywords. Hence QAS is designed to help people find specific answers to specific questions in restricted domain.

Your device activated when it heard you speak, understood the unspoken intent in the comment, executed an action and provided feedback in a well-formed English sentence, all in the space of about five seconds. The complete interaction was made possible by NLP, along with other AI elements such as machine learning and deep learning. Examples include first and last names, age, geographic locations, addresses, product type, email addresses, company name, etc. Text classification has broad applicability such as social media analysis, sentiment analysis, spam filtering, and spam detection. However, the same technologies used for social media spamming can also be used for finding important information, like an email address or automatically connecting with a targeted list on LinkedIn.

Case Grammar uses languages such as English to express the relationship between nouns and verbs by using the preposition. In 1957, Chomsky also introduced the idea of Generative Grammar, which is rule based descriptions of syntactic structures. Learn to look past all the hype and hysteria and understand what ChatGPT does and where its merits could lie for education. Mary Osborne, a professor and SAS expert on NLP, elaborates on her experiences with the limits of ChatGPT in the classroom – along with some of its merits. Businesses live in a world of limited time, limited data, and limited engineering resources. There’s often not enough time to read all the articles your boss, family, and friends send over.

At your device’s lowest levels, communication occurs not with words but through millions of zeros and ones that produce logical actions. Some industry leaders in sentiment analysis are MonkeyLearn and Repustate. If not, the process is started over again with a different set of rules. This is repeated until a specific rule is found which describes the structure of the sentence. Natural language is often ambiguous, with multiple meanings and interpretations depending on the context.

Syntactic analysis

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. Arguably one of the most well known examples of NLP, smart assistants have become increasingly integrated into our lives. Applications like Siri, Alexa and Cortana are designed to respond to commands issued by both voice and text. They can respond to your questions via their connected knowledge bases and some can even execute tasks on connected “smart” devices.

• Deep learning is a subfield of machine learning, which helps to decipher the user’s intent, words and sentences.
• Natural language processing plays a vital part in technology and the way humans interact with it.
• On predictability in language more broadly – as a 20 year lawyer I’ve seen vast improvements in use of plain English terminology in legal documents.
• This technology is still evolving, but there are already many incredible ways natural language processing is used today.

They’re also very useful for auto correcting typos, since they can often accurately guess the intended word based on context. Every Internet user has received a customer feedback survey at one point or another. While tools like SurveyMonkey and Google Forms have helped democratize customer feedback surveys, NLP offers a more sophisticated approach. The use of NLP, particularly on a large scale, also has attendant privacy issues. For instance, researchers in the aforementioned Stanford study looked at only public posts with no personal identifiers, according to Sarin, but other parties might not be so ethical.

For instance, Akkio has been used to create a chatbot that automatically predicts credit eligibility for users of a fintech service. “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. Rule-based systems rely on explicitly defined rules or heuristics to make decisions or perform tasks.

These technologies allow computers to analyze and process text or voice data, and to grasp their full meaning, including the speaker’s or writer’s intentions and emotions. As we mentioned earlier, natural language processing can yield unsatisfactory results due to its complexity and numerous conditions that need to be fulfilled. That’s why businesses are wary of NLP development, fearing that investments may not lead to desired Chat GPT outcomes. Human language is insanely complex, with its sarcasm, synonyms, slang, and industry-specific terms. All of these nuances and ambiguities must be strictly detailed or the model will make mistakes.Modeling for low resource languages. This makes it problematic to not only find a large corpus, but also annotate your own data — most NLP tokenization tools don’t support many languages.High level of expertise.

First, data goes through preprocessing so that an algorithm can work with it — for example, by breaking text into smaller units or removing common words and leaving unique ones. Once the data is preprocessed, a language modeling algorithm is developed to process it. Most commonly, rule-based or machine learning-based algorithms are used. In this article, we will explore the fundamental concepts and techniques of Natural Language Processing, shedding light on how it transforms raw text into actionable information. From tokenization and parsing to sentiment analysis and machine translation, NLP encompasses a wide range of applications that are reshaping industries and enhancing human-computer interactions. Whether you are a seasoned professional or new to the field, this overview will provide you with a comprehensive understanding of NLP and its significance in today’s digital age.

NLP plays an essential role in many applications you use daily—from search engines and chatbots, to voice assistants and sentiment analysis. NLP, meaning Natural Language Processing, is a branch of artificial intelligence (AI) that focuses on the interaction between computers and humans using human language. Its primary objective is to empower computers to comprehend, interpret, and produce human language effectively. NLP encompasses diverse tasks such as text analysis, language translation, sentiment analysis, and speech recognition. Continuously evolving with technological advancements and ongoing research, NLP plays a pivotal role in bridging the gap between human communication and machine understanding. Natural language processing can be an extremely helpful tool to make businesses more efficient which will help them serve their customers better and generate more revenue.

NLP allows automatic summarization of lengthy documents and extraction of relevant information—such as key facts or figures. This can save time and effort in tasks like research, news aggregation, and document management. Topic modeling is an unsupervised learning technique that uncovers the hidden thematic structure in large collections of documents. It organizes, summarizes, and visualizes textual data, making it easier to discover patterns and trends.

Chatbots might be the first thing you think of (we’ll get to that in more detail soon). But there are actually a number of other ways NLP can be used to automate customer service. Customer service costs businesses a great deal in both time and money, especially during growth periods. They are effectively trained by their owner and, like other applications of NLP, learn from experience in order to provide better, more tailored assistance. Next, the sense of each word is understood by using lexicons (vocabulary) and set of grammatical rules. However, certain different words are having similar meaning (synonyms) and words having more than one meaning (polysemy).

They are beneficial for eCommerce store owners in that they allow customers to receive fast, on-demand responses to their inquiries. This is important, particularly for smaller companies that don’t have the resources to dedicate a full-time customer support agent. The saviors for students and professionals alike – autocomplete and autocorrect – are prime NLP application examples. Autocomplete (or sentence completion) integrates NLP with specific Machine learning algorithms to predict what words or sentences will come next, in an effort to complete the meaning of the text.

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. It enhances our communication, bridges language barriers, aids in data interpretation, and revolutionizes educational assessments, among many others.

Part-of-speech (POS) tagging identifies the grammatical category of each word in a text, such as noun, verb, adjective, or adverb. In our example, POS tagging might label “walking” as a verb and “Apple” as a proper noun. This helps NLP systems understand the structure and meaning of sentences. The field of NLP has been around for decades, but recent advances in machine learning have enabled it to become increasingly powerful and effective.

“Say you have a chatbot for customer support, it is very likely that users will try to ask questions that go beyond the bot’s scope and throw it off. This can be resolved by having default responses in place, however, it isn’t exactly possible to predict the kind of questions a user may ask or the manner in which they will be raised. Take your omnichannel retail and eccommerce sales and customer experience to new heights with conversation analytics for deep customer insights.

ChatGPT almost immediately disturbed academics, journalists, and others because of concerns that it was impossible to distinguish human writing from ChatGPT-generated writing. Sequence to sequence models are a very recent addition to the family of models used in NLP. A sequence to sequence (or seq2seq) model takes an entire sentence or document as input (as in a document classifier) but it produces a sentence or some other sequence (for example, a computer program) as output. Human language is filled with many ambiguities that make it difficult for programmers to write software that accurately determines the intended meaning of text or voice data.

In this way, organizations can see what aspects of their brand or products are most important to their customers and understand sentiment about their products. Semantic knowledge management systems allow organizations to store, classify, and retrieve knowledge that, in turn, helps them improve their processes, collaborate within their teams, and improve understanding of their operations. Here, one of the best NLP examples is where organizations use them to serve content in a knowledge base for customers or users. See how Repustate helped GTD semantically categorize, store, and process their data.

TextBlob is a more intuitive and easy to use version of NLTK, which makes it more practical in real-life applications. Its strong suit is a language translation feature powered by Google Translate. Unfortunately, it’s also too slow for production and doesn’t have some handy features like word vectors. You can foun additiona information about ai customer service and artificial intelligence and NLP. You can be sure about one common feature — all of these tools have active discussion boards where most of your problems will be addressed and answered.

Tutorials Point is a leading Ed Tech company striving to provide the best learning material on technical and non-technical subjects. The parse tree breaks down the sentence into structured parts so that the computer can easily understand and process it. In order for the parsing algorithm to construct this parse tree, a set of rewrite rules, which describe what tree structures are legal, need to be constructed. Natural Language Processing (NLP) refers to AI method of communicating with an intelligent systems using a natural language such as English.

It uses ML (Machine Learning) to meet the objective of Artificial Intelligence. The ultimate goal is to bridge how people communicate and what computers can understand. In the beginning of the year 1990s, NLP started growing faster and achieved good process accuracy, especially in English Grammar. In 1990 also, an electronic text introduced, which provided a good resource for training and examining natural language programs. Other factors may include the availability of computers with fast CPUs and more memory. The major factor behind the advancement of natural language processing was the Internet.