8 Natural Language Processing NLP Examples

With glossary and phrase rules, companies are able to customize this AI-based tool to fit the market and context they’re targeting. Machine learning and natural language processing technology also enable IBM’s Watson Language Translator to convert spoken sentences into text, making communication that much easier. Organizations and potential customers can then interact through the most convenient language and format. You must also take note of the effectiveness of different techniques used for improving natural language processing. The advancements in natural language processing from rule-based models to the effective use of deep learning, machine learning, and statistical models could shape the future of NLP.

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. At this stage, the computer programming language is converted into an audible or textual format for the user. A financial news chatbot, for example, that is asked a question like “How is Google doing today? ” will most likely scan online finance sites for Google stock, and may decide to select only information like price and volume as its reply.

As Christina Valente, a Senior Director of Product Operations explains, “before Akkio ML, projects took months-long engineering effort, costing hundreds of thousands of dollars. With Akkio, we are able to build and deploy AI models in minutes, with no prior machine learning expertise or coding.” Sign up for a free trial of Akkio and see how NLP can help your business. A natural language is a human language, such as English or Standard Mandarin, as opposed to a constructed language, an artificial language, a machine language, or the language of formal logic.

Today, we can’t hear the word “chatbot” and not think of the latest generation of chatbots powered by large language models, such as ChatGPT, Bard, Bing and Ernie, to name a few. It’s important to understand that the content produced is not based on a human-like understanding of what was written, but a prediction of the words that might come next. In summary, Natural language processing is an exciting area of artificial intelligence development that fuels a wide range of new products such as search engines, chatbots, recommendation systems, and speech-to-text systems. As human interfaces with computers continue to move away from buttons, forms, and domain-specific languages, the demand for growth in natural language processing will continue to increase.

Natural language understanding applications

Smart assistants and chatbots have been around for years (more on this below). And while applications like ChatGPT are built for interaction and text generation, their very nature as an LLM-based app imposes some serious limitations in their ability to ensure accurate, sourced information. Where a search engine returns results that are sourced and verifiable, ChatGPT does not cite sources and may even return information that is made up—i.e., hallucinations. NLP can also help you route the customer support tickets to the right person according to their content and topic. This way, you can save lots of valuable time by making sure that everyone in your customer service team is only receiving relevant support tickets.

From predictive text to data analysis, NLP’s applications in our everyday lives are far-ranging. Democratization of artificial intelligence means making AI available for all… POS tags contain verbs, adverbs, nouns, and adjectives that help indicate the meaning of words in a grammatically correct way in a sentence.

Search Engine Results

There are more than 6,500 languages in the world, all of them with their own syntactic and semantic rules. All this business data contains a wealth of valuable insights, and NLP can quickly help businesses discover what those insights are. The “bag” part of the name refers to the fact that it ignores the order in which words appear, and instead looks only at their presence or absence in a sentence. 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. Natural Language Processing has created the foundations for improving the functionalities of chatbots.

Duplicate detection collates content re-published on multiple sites to display a variety of search results. There’s also some evidence that so-called “recommender systems,” which are often assisted by NLP technology, may exacerbate the digital siloing effect. In this example, above, the results show that customers are highly satisfied with aspects like Ease of Use and Product UX (since most of these responses are from Promoters), while they’re not so happy with Product Features. Intermediate tasks (e.g., part-of-speech tagging and dependency parsing) have not been needed anymore. Although rule-based systems for manipulating symbols were still in use in 2020, they have become mostly obsolete with the advance of LLMs in 2023.

Customer Service Automation

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. These models can be written in languages like Python, or made with AutoML tools like Akkio, Microsoft Cognitive Services, and Google Cloud Natural Language. Email service providers have evolved far beyond simple spam classification, however.

In the past years, she came up with many clever ideas that brought scalability, anonymity and more features to the open blockchains. 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. After this problem appeared in so many of my projects, I wrote my own Python package called localspelling which allows a user to convert all text in a document to British or American, or to detect which variant is used in the document.

Below is a parse tree for the sentence “The thief robbed the apartment.” Included is a description of the three different information types conveyed by the sentence. Natural language is the way we use words, phrases, and grammar to communicate with each other. This natural language examples means your team has more time to hone their ecommerce strategy while the algorithm does the brunt of the merchandising work needed to satisfy and convert user queries. A practical example of this NLP application is Sprout’s Suggestions by AI Assist feature.

For years, trying to translate a sentence from one language to another would consistently return confusing and/or offensively incorrect results. This was so prevalent that many questioned if it would ever be possible to accurately translate text. Email filters are common NLP examples you can find online across most servers. From a corporate perspective, spellcheck helps to filter out any inaccurate information in databases by removing typo variations.

Take sentiment analysis, for example, which uses natural language processing to detect emotions in text. This classification task is one of the most popular tasks of NLP, often used by businesses to automatically detect brand sentiment on social media. Analyzing these interactions can help brands detect urgent customer issues that they need to respond to right away, or monitor overall customer satisfaction. NLP is becoming increasingly essential to businesses looking to gain insights into customer behavior and preferences. Sentiment analysis is an example of how natural language processing can be used to identify the subjective content of a text.

Translation models can even be trained for particular domains to increase the accuracy of the translation. Next, teach your machine to recognize pop-culture allusions and ordinary names by highlighting movie titles, important people or places, and so on that may appear in the document. Person, place, monetary value, amount, organization, and movie are the subcategories.

ChatGPT is the fastest growing application in history, amassing 100 million active users in less than 3 months. And despite volatility of the technology sector, investors have deployed $4.5 billion into 262 generative AI startups. NLP combines AI with computational linguistics and computer science to process human or natural languages and speech. You can foun additiona information about ai customer service and artificial intelligence and NLP. The first task of NLP is to understand the natural language received by the computer. The computer uses a built-in statistical model to perform a speech recognition routine that converts the natural language to a programming language.

Akkio, an end-to-end machine learning platform, is making it easier for businesses to take advantage of NLP technology. 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. 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.

From translation and order processing to employee recruitment and text summarization, here are more NLP examples and applications across an array of industries. For processing large amounts of data, C++ and Java are often preferred because they can support more efficient code. 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. Only then can NLP tools transform text into something a machine can understand.

Have you ever wondered how Siri or Google Maps acquired the ability to understand, interpret, and respond to your questions simply by hearing your voice? The technology behind this, known as natural language processing (NLP), is responsible for the features that allow technology to come close to human interaction. MonkeyLearn is a good example of a tool that uses NLP and machine learning to analyze survey results.

First of all, NLP can help businesses gain insights about customers through a deeper understanding of customer interactions. Natural language processing offers the flexibility for performing large-scale data analytics that could improve the decision-making abilities of businesses. NLP could help businesses with an in-depth understanding of their target markets.

When we ask questions of these virtual assistants, NLP is what enables them to not only understand the user’s request, but to also respond in natural language. NLP applies both to written text and speech, and can be applied to all human languages. Other examples of tools powered by NLP include web search, email spam filtering, automatic translation of text or speech, document summarization, sentiment analysis, and grammar/spell checking. For example, some email programs can automatically suggest an appropriate reply to a message based on its content—these programs use NLP to read, analyze, and respond to your message.

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. In addition, it can offer autocorrect suggestions and even learn new words that you type frequently. By extracting meaning from written text, NLP allows businesses to gain insights about their customers and respond accordingly. The theory of universal grammar proposes that all-natural languages have certain underlying rules that shape and limit the structure of the specific grammar for any given language.

As natural language processing is making significant strides in new fields, it’s becoming more important for developers to learn how it works. A creole such as Haitian Creole has its own grammar, vocabulary and literature. It is spoken by over 10 million people worldwide and is one of the two official languages of the Republic of Haiti. Levity is a tool that allows you to train AI models on images, documents, and text data. You can rebuild manual workflows and connect everything to your existing systems without writing a single line of code.‍If you liked this blog post, you’ll love Levity.

Real-World Examples of Natural Language Processing (NLP)

One of the best ways to understand NLP is by looking at examples of natural language processing in practice. Expert.ai’s NLP platform gives publishers and content producers the power to automate important categorization and metadata information through the use of tagging, creating a more engaging and personalized experience for readers. Publishers and information service providers can suggest content to ensure that users see the topics, documents or products that are most relevant to them. First, the capability of interacting with an AI using human language—the way we would naturally speak or write—isn’t new.

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.

Even the business sector is realizing the benefits of this technology, with 35% of companies using NLP for email or text classification purposes. Additionally, strong email filtering in the workplace can significantly reduce the risk of someone clicking and opening a malicious email, thereby limiting the exposure of sensitive data. 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. NPL cross-checks text to a list of words in the dictionary (used as a training set) and then identifies any spelling errors.

They rely on a combination of advanced NLP and natural language understanding (NLU) techniques to process the input, determine the user intent, and generate or retrieve appropriate answers. NLP enables automatic categorization of text documents into predefined classes or groups based on their content. This is useful for tasks like spam filtering, sentiment analysis, and content recommendation.

Classification and clustering are extensively used in email applications, social networks, and user generated content (UGC) platforms. Through AI, fields like machine learning and deep learning are opening eyes to a world of all possibilities. Machine learning is increasingly being used in data analytics to make sense of big data. It is also used to program chatbots to simulate human conversations with customers. However, these forward applications of machine learning wouldn’t be possible without the improvisation of Natural Language Processing (NLP).

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field. 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.

• Most important of all, you should check how natural language processing comes into play in the everyday lives of people.
• Social listening provides a wealth of data you can harness to get up close and personal with your target audience.
• We also score how positively or negatively customers feel, and surface ways to improve their overall experience.
• NLP can be challenging to implement correctly, you can read more about that here, but when’s it’s successful it offers awesome benefits.

Today, Google Translate covers an astonishing array of languages and handles most of them with statistical models trained on enormous corpora of text which may not even be available in the language pair. Transformer models have allowed tech giants to develop translation systems trained solely on monolingual text. 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. Nowadays the more sophisticated spellcheckers use neural networks to check that the correct homonym is used.

Generally, computer-generated content lacks the fluidity, emotion and personality that makes human-generated content interesting and engaging. However, NLG can be used with NLP to produce humanlike text in a way that emulates a human writer. This is done by identifying the main topic of a document and then using NLP to determine the most appropriate way to write the document in the user’s native language.

• It can speed up your processes, reduce monotonous tasks for your employees, and even improve relationships with your customers.
• The query simply has too many words that are difficult to interpret without context.
• While there are many challenges in natural language processing, the benefits of NLP for businesses are huge making NLP a worthwhile investment.
• It helps you understand how positive or negative the sentiment of the data is.
• Perhaps surprisingly, the fine-tuning datasets can be extremely small, maybe containing only hundreds or even tens of training examples, and fine-tuning training only requires minutes on a single CPU.

Another remarkable thing about human language is that it is all about symbols. According to Chris Manning, a machine learning professor at Stanford, it is a discrete, symbolic, categorical signaling system. NLP is an exciting and rewarding discipline, and has potential to profoundly impact the world in many positive ways. Unfortunately, NLP is also the focus of several controversies, and understanding them is also part of being a responsible practitioner. For instance, researchers have found that models will parrot biased language found in their training data, whether they’re counterfactual, racist, or hateful.

NLP uses NLU to analyze and interpret data while NLG generates personalized and relevant content recommendations to users. Its ability to understand the intricacies of human language, including context and cultural nuances, makes it an integral part of AI business intelligence tools. 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.

Businesses use large amounts of unstructured, text-heavy data and need a way to efficiently process it. Much of the information created online and stored in databases is natural human language, and until recently, businesses couldn’t effectively analyze this data. NLP uses either rule-based or machine learning approaches to understand the structure and meaning of text. 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. 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.