8 Natural Language Processing NLP Examples

In one case, Akkio was used to classify the sentiment of tweets about a brand’s products, driving real-time customer feedback and allowing companies to adjust their marketing strategies accordingly. If a negative sentiment is detected, companies can quickly address customer needs before the situation escalates. More than a mere tool of convenience, it’s driving serious technological breakthroughs. Natural language processing plays a vital part in technology and the way humans interact with it.

Make the most out of your untapped business and customer data with this guide to the nine best text classification examples. 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. 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.

Smart Assistants

Based on the requirements established, teams can add and remove patients to keep their databases up to date and find the best fit for patients and clinical trials. Sentiment Analysis is also widely used on Social Listening processes, on platforms such as Twitter. This helps organisations discover what the brand image of their company really looks like through analysis the sentiment of their users’ feedback on social media platforms. MonkeyLearn can help you build your own natural language processing models that use techniques like keyword extraction and sentiment analysis. These are the most common natural language processing examples that you are likely to encounter in your day to day and the most useful for your customer service teams. 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.

For example, the combination ch is common in English, Dutch, Spanish, German, French, and other languages. An NLP system can look for stopwords (small function words such as the, at, in) in a text, and compare with a list of known stopwords for many languages. The language with the most stopwords in the unknown text is identified as the language. So a document with many occurrences of le and la is likely to be French, for example. We offer a range of NLP datasets on our marketplace, perfect for research, development, and various NLP tasks. Similarly, ticket classification using NLP ensures faster resolution by directing issues to the proper departments or experts in customer support.

As we delve into specific Natural Language Processing examples, you’ll see firsthand the diverse and impactful ways NLP shapes our digital experiences. Early attempts at machine translation during the Cold War era marked its humble beginnings. Whether reading text, comprehending its meaning, or generating human-like responses, NLP encompasses a wide range of tasks.

Natural language processing can be used for topic modelling, where a corpus of unstructured text can be converted to a set of topics. You can read more about k-means and Latent Dirichlet Allocation in my review of the 26 most important data science concepts. Traditional Business Intelligence (BI) tools such as Power BI and Tableau allow analysts to get insights out of structured databases, allowing them to see at a glance which team made the most sales in a given quarter, for example.

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. Texting is convenient, but if you want to interact with a computer it’s often faster and easier to simply speak. That’s why smart assistants like Siri, Alexa and Google Assistant are growing increasingly popular. 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.

Runestone Academy can only continue if we get support from individuals like you. Our mission is to provide great books to you for free, but we ask that you consider a $10 donation, more if you can or less if $10 is a burden. IBM has launched a new open-source toolkit, PrimeQA, to spur progress in multilingual question-answering systems to make it easier for anyone to quickly find information on the web. Watch IBM Data & AI GM, Rob Thomas as he hosts NLP experts and clients, showcasing how NLP technologies are optimizing businesses across industries. Modelling risk and cost in clinical trials with NLP Fast Data Science’s Clinical Trial Risk Tool Clinical trials are a vital part of bringing new drugs to market, but planning and running them can be a complex and expensive process. Let’s analyze some Natural Language Processing examples to see its true power and potential.

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. Today, employees and customers alike expect the same ease of finding what they need, when they need it from any search bar, and this includes within the enterprise.

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. NLP is becoming increasingly essential to businesses looking to gain insights into customer behavior and preferences. By applying NLP techniques, companies can identify trends and customer feedback in order to better understand their customers, improve their products and services, create more engaging content, and analyze large amounts of unstructured data. Another one of the common NLP examples is voice assistants like Siri and Cortana that are becoming increasingly popular. These assistants use natural language processing to process and analyze language and then use natural language understanding (NLU) to understand the spoken language.

However, as you are most likely to be dealing with humans your technology needs to be speaking the same language as them. When you send out surveys, be it to customers, employees, or any other group, you need to be able to draw actionable insights from the data you get back. 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.

Top 10 Word Cloud Generators

Finally, they use natural language generation (NLG) which gives them the ability to reply and give the user the required response. You can foun additiona information about ai customer service and artificial intelligence and NLP. Voice command activated assistants still have a long way to go before they become secure and more efficient due to their many vulnerabilities, which data scientists are working on. Sentiment analysis is an example of how natural language processing can be used to identify the subjective content of a text. Sentiment analysis has been used in finance to identify emerging trends which can indicate profitable trades.

Compared to chatbots, smart assistants in their current form are more task- and command-oriented. 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. Plus, tools like MonkeyLearn’s interactive Studio dashboard (see below) then allow you to see your analysis in one place – click the link above to play with our live public demo.

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. Natural language processing (NLP) is the ability of a computer program to understand human language as it’s spoken and written — referred to as natural language. Many companies have more data than they know what to do with, making it challenging to obtain meaningful insights.

It can speed up your processes, reduce monotonous tasks for your employees, and even improve relationships with your customers. NLP systems can streamline business operations by automating employees’ workflows. While natural language processing may initially appear complex, it is surprisingly user-friendly. In fact, there’s a good chance that you already use it in your day-to-day life to transcribe audio into text. Once you familiarize yourself with a few natural language examples and grasp the personal and professional benefits it offers, you’ll never revert to traditional transcription methods again. NLP is a branch of Artificial Intelligence that deals with understanding and generating natural language.

• Companies can then apply this technology to Skype, Cortana and other Microsoft applications.
• Because NLP tools are so easy and quick to use, you can scale your content creation and business much quicker than before without hiring more staff members.
• 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.
• Additionally, if your transcription software supports translation, you can identify the language preferences of your viewers and tailor your strategy accordingly.
• If someone says, “The

  other shoe fell”, there is probably no shoe and nothing falling.

However, trying to track down these countless threads and pull them together to form some kind of meaningful insights can be a challenge. This function predicts what you might be searching for, so you can simply click on it and save yourself the hassle of typing it out. IBM’s Global Adoption Index cited that almost half of businesses surveyed globally are using some kind of application powered by NLP.

In addition, artificial neural networks can automate these processes by developing advanced linguistic models. Teams can then organize extensive data sets at a rapid pace and extract essential insights through NLP-driven searches. Microsoft has explored the possibilities of machine translation with Microsoft Translator, which translates written and spoken sentences across various formats. Not only does this feature process text and vocal conversations, but it also translates interactions happening on digital platforms. Companies can then apply this technology to Skype, Cortana and other Microsoft applications. Through projects like the Microsoft Cognitive Toolkit, Microsoft has continued to enhance its NLP-based translation services.

NLP tools can automatically produce more accurate translations because they’re trained using more natural text and speech data. They can recognize your natural speech as it is and produce output as close to natural written language as possible. What used to be a tedious manual process that took days for a human to do can now be done in mere minutes with the help of NLP.

This means you can save time on creating video captions, website posts, and any other content uses you have for your transcriptions. If you’re currently trying to grow your company, the good news is that you can spend the time you save on other, more strategic tasks in your business. Natural language processing (NLP) pertains to computers and machines comprehending and processing language in a manner akin to human speech and writing. Unlike humans, who inherently grasp the existence of linguistic rules (such as grammar, syntax, and punctuation), computers require training to acquire this understanding.

What is natural language processing?

This feature essentially notifies the user of any spelling errors they have made, for example, when setting a delivery address for an online order. 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. Then, the user has the option to correct the word automatically, or manually through spell check.

As a result, many businesses now look to NLP and text analytics to help them turn their unstructured data into insights. Core NLP features, such as named entity extraction, give users the power to identify key elements like names, dates, currency values, and even phone numbers in text. 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. MonkeyLearn is a good example of a tool that uses NLP and machine learning to analyze survey results. It can sort through large amounts of unstructured data to give you insights within seconds.

Sentiment Analysis

People go to social media to communicate, be it to read and listen or to speak and be heard. As a company or brand you can learn a lot about how your customer feels by what they comment, post about or listen to. Leveraging NLP for video transcription not only enables you to enhance business decision-making but also empowers you to optimize audience engagement.

Though it has its challenges, NLP is expected to become more accurate with more sophisticated models, more accessible and more relevant in numerous industries. I often work using an open source library such as Apache Tika, which is able to convert PDF documents https://chat.openai.com/ into plain text, and then train natural language processing models on the plain text. 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.

You may be a business owner wondering, “What are some applications of natural language processing? ” Fortunately, NLP has many applications and benefits that help business owners save time and money and move closer to their strategic goals. There has recently been a lot of hype about transformer models, which are the latest iteration of neural networks. Transformers are able to represent the grammar of natural language in an extremely deep and sophisticated way and have improved performance of document classification, text generation and question answering systems. However, NLP has reentered with the development of more sophisticated algorithms, deep learning, and vast datasets in recent years.

Every time you get a personalized product recommendation or a targeted ad, there’s a good chance NLP is working behind the scenes. Today’s consumers crave seamless interactions, and NLP-powered chatbots or virtual assistants are stepping up. If you used a tool to translate it instantly, you’ve engaged with Natural Language Processing.

Once professionals have adopted Covera Health’s platform, it can quickly scan images without skipping over important details and abnormalities. Healthcare workers no longer have to choose between speed and in-depth analyses. Instead, the platform is able to provide more accurate diagnoses and ensure patients receive the correct treatment while cutting down visit times in the process. Three open source tools commonly used for natural language processing include Natural Language Toolkit (NLTK), Gensim and NLP Architect by Intel.

They then learn on the job, storing information and context to strengthen their future responses. 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. 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. Modern email filter systems leverage Natural Language Processing (NLP) to analyze email content, intelligently categorize messages, and streamline your inbox. By identifying keywords and message intent, NLP ensures spam and unwanted messages are kept at bay while facilitating effortless email retrieval.

By understanding and leveraging its potential, companies are poised to not only thrive in today’s competitive market but also pave the way for future innovations. For instance, by analyzing user reviews, companies can identify areas of improvement or even new product opportunities, all by interpreting customers’ voice. By understanding NLP’s essence, you’re not only getting a grasp on a pivotal AI subfield but also appreciating the intricate dance between human cognition and machine learning. Now, thanks to AI and NLP, algorithms can be trained on text in different languages, making it possible to produce the equivalent meaning in another language.

This technology even extends to languages like Russian and Chinese, which are traditionally more difficult to translate due to their different alphabet structure and use of characters instead of letters. With the recent focus on large language models (LLMs), AI technology in the language domain, which includes NLP, is now benefiting similarly. You may not realize it, but there are countless real-world examples of NLP techniques that impact our everyday lives.

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.

In order to streamline certain areas of your business and reduce labor-intensive manual work, it’s essential to harness the power of artificial intelligence. Predictive text has become so ingrained in our day-to-day lives that we don’t often think about what is going on behind the scenes. As the name suggests, predictive text works by predicting what you are about to write. Over time, predictive text learns from you and the language you use to create a personal dictionary. Organizing and analyzing this data manually is inefficient, subjective, and often impossible due to the volume. Smart search is another tool that is driven by NPL, and can be integrated to ecommerce search functions.

If someone says, “The

other shoe fell”, there is probably no shoe and nothing falling. When you read a sentence in English or a statement in a formal language, you

have to figure out what the structure of the sentence is (although in a natural

language you do this subconsciously). Accelerate the business value of artificial intelligence with a powerful and flexible portfolio of libraries, services and applications. 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.

If you’re translating your subtitles, they can also help people who speak a different language understand your content. 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. Natural Chat PG languages are full of ambiguity, which people deal with by

using contextual clues and other information. Formal languages are

designed to be nearly or completely unambiguous, which means that any

statement has exactly one meaning, regardless of context. The science of identifying authorship from unknown texts is called forensic stylometry.

Here are eight examples of applications of natural language processing which you may not know about. If you have a large amount of text data, don’t hesitate to hire an NLP consultant such as Fast Data Science. Oftentimes, when businesses need help understanding their customer needs, they turn to sentiment analysis.

Chatbots do all this by recognizing the intent of a user’s query and then presenting the most appropriate response. Deep 6 AI developed a platform that uses machine learning, NLP and AI to improve clinical trial processes. Healthcare professionals use the platform to sift through structured and unstructured data sets, determining ideal patients through concept mapping and criteria gathered from health backgrounds.

The abundance of AI tools in the market brings the added advantage of natural language processing capabilities. By converting the text into numerical vectors (using techniques like word embeddings) and feeding those vectors into machine learning models, it’s possible to uncover previously hidden insights from these “dark data” sources. In our globalized economy, the ability to quickly and accurately translate text from one language to another has become increasingly important. NLP algorithms focus on linguistics, computer science, and data analysis to provide machine translation capabilities for real-world applications. Bag-of-words, for example, is an algorithm that encodes a sentence into a numerical vector, which can be used for sentiment analysis. With automatic summarization, NLP algorithms can summarize the most relevant information from content and create a new, shorter version of the original content.

Certain subsets of AI are used to convert text to image, whereas NLP supports in making sense through text analysis. From a corporate perspective, spellcheck helps to filter out any inaccurate information in databases by removing typo variations. Thanks to NLP, you can analyse your survey example of natural language responses accurately and effectively without needing to invest human resources in this process. As of 1996, there were 350 attested families with one or more native speakers of Esperanto. Latino sine flexione, another international auxiliary language, is no longer widely spoken.

Businesses can tailor their marketing strategies by understanding user behavior, preferences, and feedback, ensuring more effective and resonant campaigns. Natural Language Processing isn’t just a fascinating field of study—it’s a powerful tool that businesses across sectors leverage for growth, efficiency, and innovation. Each of these Natural Language Processing examples showcases its transformative capabilities. As technology evolves, we can expect these applications to become even more integral to our daily interactions, making our experiences smoother and more intuitive.

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. 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. Optical Character Recognition (OCR) automates data extraction from text, either from a scanned document or image file to a machine-readable text.

This tool learns about customer intentions with every interaction, then offers related results. However, it has come a long way, and without it many things, such as large-scale efficient analysis, wouldn’t be possible. Natural Language Processing (NLP) is at work all around us, making our lives easier at every turn, yet we don’t often think about it.

Employee-recruitment software developer Hirevue uses NLP-fueled chatbot technology in a more advanced way than, say, a standard-issue customer assistance bot. In this case, the bot is an AI hiring assistant that initializes the preliminary job interview process, matches candidates with best-fit jobs, updates candidate statuses and sends automated SMS messages to candidates. Because of this constant engagement, companies are less likely to lose well-qualified candidates due to unreturned messages and missed opportunities to fill roles that better suit certain candidates.