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11 NLP Applications & Examples in Business

They use highly trained algorithms that, not only search for related words, but for the intent of the searcher. Results often change on a daily basis, following trending queries and morphing right along with human language. They even learn to suggest topics and subjects related to your query that you may not have even realized you were interested in. Natural language processing and powerful machine learning algorithms (often multiple used in collaboration) are improving, and bringing order to the chaos of human language, right down to concepts like sarcasm. We are also starting to see new trends in NLP, so we can expect NLP to revolutionize the way humans and technology collaborate in the near future and beyond.

natural language processing examples

Because the data is unstructured, it’s difficult to find patterns and draw meaningful conclusions. Tom and his team spend much of their day poring over paper and digital documents to detect trends, patterns, and activity that could raise red flags. I’ve already alluded to how much information is wrapped up in human language, whether written or spoken. For some sectors – I’m thinking of the legal system as a prime example – the ability to easily extract key information from thousands of pages of documents could be a real game-changer. Tools such as MeaningCloud and ML Analyzer can automatically summarize long documents into short, fluent, and accurate summaries. In English and many other languages, a single word can take multiple forms depending upon context used.

NLP Example for Converting Spelling between US and UK English

These are the most common that you are likely to encounter in your day to day and the most useful for your customer service teams. Natural Language Processing APIs allow developers to integrate human-to-machine communications and complete several useful tasks such as speech recognition, chatbots, spelling correction, sentiment analysis, etc. Many natural language processing tasks involve syntactic and semantic analysis, used to break down human language into machine-readable chunks. In this guide, you’ll learn about the basics of Natural Language Processing and some of its challenges, and discover the most popular NLP applications in business. Finally, you’ll see for yourself just how easy it is to get started with code-free natural language processing tools.

Stemming normalizes the word by truncating the word to its stem word. For example, the words “studies,” “studied,” “studying” will be reduced to “studi,” making all these word forms to refer to only one token. Notice that stemming may not give us a dictionary, grammatical word for a particular set of words. As shown above, the final graph has many useful words that help us understand what our sample data is about, showing how essential it is to perform data cleaning on NLP. Next, we are going to remove the punctuation marks as they are not very useful for us. We are going to use isalpha( ) method to separate the punctuation marks from the actual text.


The average cost of an internal security breach in 2018 was $8.6 million. As organizations grow, they are more vulnerable to security breaches. With more and more consumer data being collected for market research, it is more important than ever for businesses to keep their data safe. With NLP-powered customer support chatbots, organizations have more bandwidth to focus on future product development. And it’s not just customer-facing interactions; large-scale organizations can use NLP chatbots for other purposes, such as an internal wiki for procedures or an HR chatbot for onboarding employees.

natural language processing examples

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. The possibility of translating text and speech to different languages has always been one of the main interests in the NLP field. From the first attempts to translate text from Russian to English in the 1950s to state-of-the-art deep learning neural systems, machine translation (MT) has seen significant improvements but still presents challenges.

Install and Load Main Python Libraries for NLP

Scalenut is an NLP-based content marketing and SEO tool that helps marketers from every industry create attractive, engaging, and delightful content for their customers. In addition to monitoring, an NLP data system can automatically classify new documents and set up user access based on systems that have already been set up for user access and document classification. Customer chatbots work on real-life customer interactions without human intervention after being trained with a predefined set of instructions and specific solutions to common problems. Whether it is to play our favorite song or search for the latest facts, these smart assistants are powered by NLP code to help them understand spoken language.

natural language processing examples

In this case, we define a noun phrase by an optional determiner followed by adjectives and nouns. Notice that we can also visualize the text with the .draw( ) function. In the code snippet below, many of the words after stemming did not end up being a recognizable dictionary word. Notice that the most used words are punctuation marks and stopwords. In the example above, we can see the entire text of our data is represented as sentences and also notice that the total number of sentences here is 9. By tokenizing the text with sent_tokenize( ), we can get the text as sentences.

NLP can address critical government issues

Elicit is designed for a growing number of specific tasks relevant to research, like summarization, data labeling, rephrasing, brainstorming, and literature reviews. While solving NLP problems, it is always good to start with the prebuilt Cognitive Services. When the needs are beyond the bounds of the prebuilt cognitive service and when you want to search for custom machine learning methods, you will find this repository very useful. To get started, navigate to the Setup Guide, which lists instructions on how to setup your environment and dependencies. Translation company Welocalize customizes Googles AutoML Translate to make sure client content isn’t lost in translation. This type of natural language processing is facilitating far wider content translation of not just text, but also video, audio, graphics and other digital assets.

  • Learn how these insights helped them increase productivity, customer loyalty, and sales revenue.
  • Notice that the most used words are punctuation marks and stopwords.
  • It converts a large set of text into more formal representations such as first-order logic structures that are easier for the computer programs to manipulate notations of the natural language processing.
  • There has recently been a lot of hype about transformer models, which are the latest iteration of neural networks.
  • Now, however, it can translate grammatically complex sentences without any problems.

I’ve found — not surprisingly — that Elicit works better for some tasks than others. Tasks like data labeling and summarization are still rough around the edges, with noisy results and spotty accuracy, but research from Ought and research from OpenAI shows promise for the future. “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. The startup is using artificial intelligence to allow “companies to solver hard problems, faster.” Although details have not been released, Project UV predicts it will alter how engineers work. You may have seen predictive text pop up in an email you’re drafting on Gmail, or even in a text you’re crafting.

Lexical semantics (of individual words in context)

Responsibility for the information and views expressed herein lies entirely with the authors. You use a dispersion plot when you want to see natural language processing examples where words show up in a text or corpus. If you’re analyzing a single text, this can help you see which words show up near each other.

Entities can be names, places, organizations, email addresses, and more. It involves filtering out high-frequency words that add little or no semantic value to a sentence, for example, which, to, at, for, is, etc. When we refer to stemming, the root form of a word is called a stem.

Natural Language Processing (NLP): 7 Key Techniques

Parts of speech(PoS) tagging is crucial for syntactic and semantic analysis. Therefore, for something like the sentence above, the word “can” has several semantic meanings. The second “can” at the end of the sentence is used to represent a container. Giving the word a specific meaning allows the program to handle it correctly in both semantic and syntactic analysis. Let’s dig deeper into natural language processing by making some examples. Now that you have learnt about various NLP techniques ,it’s time to implement them.

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