14 Natural Language Processing Examples NLP Examples

A key responsibility of the CIO is to stay ahead of disruptions. Learn about digital transformation tools that could help secure … This is when words are marked based on the part-of speech they are — such as nouns, verbs and adjectives. This is when common words are removed from text so unique words that offer the most information about the text remain. Text Analysis API by AYLIEN is used to derive meaning and insights from the textual content. It is available for both free as well as paid from$119 per month.

In today’s world, this level of example of nlping can help improve both the quality of living for people from all walks of life and enhance the experiences businesses offer their customers through digital interactions. 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. 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.

Benefits of natural language processing

Next, we are going to use IDF values to get the closest answer to the query. Notice that the word dog or doggo can appear in many many documents. However, if we check the word “cute” in the dog descriptions, then it will come up relatively fewer times, so it increases the TF-IDF value.

• It falls under the AI umbrella, along with machine learning and deep learning .
• 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.
• It’s not always easy to explain natural language processing, which can sometimes lead to confusion.
• A bag of words model converts the raw text into words, and it also counts the frequency for the words in the text.
• For instance, if your customers are making a repeated typo for the word “pajamas” and typing “pajama” instead, a smart search bar will recognize that “pajama” also means “pajamas,” even without the “s” at the end.
• Connect with your customers and boost your bottom line with actionable insights.

Syntactic analysis involves the analysis of words in a sentence for grammar and arranging words in a manner that shows the relationship among the words. For instance, the sentence “The shop goes to the house” does not pass. So, it’s no surprise that there can be a general disconnect between computers and humans. Since computers cannot communicate as organically as we do, we might even assume this separation between the two is larger than it actually is. Natural language processing is a fascinating area that already offers many benefits to our daily lives.

Machine Translation

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. Natural language processing is one of the most exciting aspects of machine learning and artificial intelligence. In this blog, we bring you 14 NLP examples that will help you understand the use of natural language processing and how it is beneficial to businesses.

As a result, companies with global audiences can adapt their content to fit a range of cultures and contexts. NLP can be used to great effect in a variety of business operations and processes to make them more efficient. One of the best ways to understand NLP is by looking at examples of natural language processing in practice. One of the tell-tale signs of cheating on your Spanish homework is that grammatically, it’s a mess. Many languages don’t allow for straight translation and have different orders for sentence structure, which translation services used to overlook. With NLP, online translators can translate languages more accurately and present grammatically-correct results.

Word Sense Disambiguation

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. 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. NLP is not perfect, largely due to the ambiguity of human language. However, it has come a long way, and without it many things, such as large-scale efficient analysis, wouldn’t be possible. To successfully run these notebooks, you will need an Azure subscription or can try Azure for free.

To improve automation efforts and streamline the manufacturing pipeline, NLP can analyze thousands of shipment documents and give manufacturers better insight into what areas of their supply chain are lagging. Using this info, they can make upgrades to certain steps in the process or make logistical changes to optimize efficiency. Most higher-level NLP applications involve aspects that emulate intelligent behaviour and apparent comprehension of natural language.

Text Analysis with Machine Learning

In this post, I’ll go over four functions of artificial intelligence and natural language processing and give examples of tools and services that use them. NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly—even in real time. There’s a good chance you’ve interacted with NLP in the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences. But NLP also plays a growing role in enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processes.

A different formula calculates the actual output from our program. First, we will see an overview of our calculations and formulas, and then we will implement it in Python. Notice that the first description contains 2 out of 3 words from our user query, and the second description contains 1 word from the query.