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12 Applications of Natural Language Processing

Aug 23, 2021

What is Natural Language Processing?

Have you ever texted someone and had autocorrect kick in to change a misspelled word before you hit send? Or been to a foreign country and used a digital language translator to help you communicate? How about watching a YouTube video with captions, which were likely created using Caption Generation? These are just a few examples of natural language processing in action and how this technology impacts our lives.

Natural language processing is an aspect of artificial intelligence that analyzes data to gain a greater understanding of natural human language. Using machine learning, natural language processing—or NLP—is focused on processing the nuances of how we communicate with one another, so that it can recreate how we speak to and with one another without the usual rules of English getting in the way. NLP can affect a multitude of digital communications including email, online chats and messaging, social media posts, and more.

Because NLP is becoming a hugely influential aspect of the IT industry, those currently involved or interested in pursuing a career in information technology should learn as much as possible about NLP. With NLP permeating so many different parts of our technological lives, it’s likely to be considered an integral part of any IT job.

Examples of Natural Language Processing

1. Autocorrect and Spell-check

In addition to making sure you don’t text the wrong word to your friends and colleagues, NLP can also auto correct your misspelled words in programs such as Microsoft Word. Similarly, it can assist you in attaining perfect grammar both in Word and using additional tools such as Grammarly. No matter which tool you use, NLP can help you become a better writer. 

2. Text Classification

If you’ve ever answered a survey—or administered one as part of your job—chances are NLP helped you organize the responses so they can be managed and analyzed. NLP can easily categorize this data in a fraction of the time it would take to do so manually—and even categorize it to exacting specifications, such as topic or theme. Text classification can also be used in spam filtering, genre classification, and language identification.

3. Sentiment Analysis

Anyone who has ever misread the tone of a text or email knows how challenging it can be to translate sarcasm, irony, or other nuances of communication that are easily picked up on in face-to-face conversation. This is where sentiment analysis comes in. Sentiment analysis can analyze patterns of speech and conversation to help filter negative conversations on social media, analyze consumer feedback on a brand’s marketing campaign, and even help businesses determine public sentiment about their company’s image.

4.  Question Answering

NLP is also a driving force behind programs designed to answer questions, often in support of customer service initiatives. For example, if you’ve ever used a chatbot on a website to help get additional information or support about a product or service or employed a virtual assistant to work your way through a technical issue before you’re routed to an IT team, you are using question-answering platforms that rely on NLP. Backed by AI, question answering platforms can also learn from each consumer interaction, which allows them to improve interactions over time.

5.  Caption Generation

Above, we’d mentioned the use of caption generation to help create captions for YouTube videos, which is helpful for disabled individuals who may need additional support to consume media. But this form of NLP has other applications, too. Caption generation also helps to describe images on the internet, allowing those using a text reader for online surfing to “hear” what images are illustrating the page they’re reading. This makes the digital world easier to navigate for disabled individuals of all kinds. 

6.  Text Extraction

Text extraction also has a variety of uses that can help IT and business professionals alike. Text extraction can be used to scan for specific identifying information across customer communications or support tickets, making it easier to route requests or search for select incidences. It can also single out specific models or serial numbers to keep track of products, assist in data aggregation using selected information identifiers, and even identify common statements made by consumers in digital communications.

7.  Text Prediction

You may have seen predictive text pop up in an email you’re drafting on Gmail, or even in a text you’re crafting. Autocorrect is another example of text prediction that marks or changes misspellings or grammatical mistakes in Word documents. Text prediction also shows up in your Google search bar, attempting to determine what you’re looking for before you finish typing your search term. NLP is the power behind each of these instances of text prediction, which also learns by your examples to perfect its capabilities the more you use it. 

8.   Machine Translation

Machine translation is exactly what it sounds like—the ability to translate text from one language to another—in a program such as Google Translate. NLP first rose to prominence as the backbone of machine translation and is considered one of the most important applications of NLP. It has advanced dramatically since its inception, thanks to an abundance of data and growth in the field of neural networks, and now supports businesses in foreign translations, travelers interested in improving their vacation experiences, and more. 

9.  Text Summarization

There are two different kinds of text summarization. Extraction-based summarization creates a summary based on key phrases, while abstraction-based summarization creates a summary based on paraphrasing the existing content—the latter of which is used more often. Think of text summarization as meta data or a quick hit of information that can give you the gist of longer content such as a news report, legal document, or other similarly lengthy information.

10.  Market Intelligence

Marketers are always looking for ways to analyze customers, and NLP helps them do so through market intelligence. Market intelligence can hunt through unstructured data for patterns that help identify trends that marketers can use to their advantage, including keywords and competitor interactions. Using this information, marketers can help companies refine their marketing approach and make a bigger impact.

11.  Intent Classification

Intent classification is similar to sentiment analysis in that it can identify the underlying meaning or message behind digital communications. However, it differs by acting as a powerful tool for marketers and businesses who want to zero in on prospective customers or identify customer pain points they can address with better service. Emails, chatbots, and other forms of digital content can help businesses and marketers locate consumers who are primed to purchase, while social media commentary or customer support communications can help identify product or service issues they must address.

12.  Urgency Detection

NLP also helps fuel urgency detection, which identifies certain expressions, words and phrases that typically showcase concern or immediate need. This comes in particularly handy in IT service situations, when customers or colleagues often communicate with help desks via email, chatbot, or Slack, so support teams can prioritize requests and more effectively manage their workload.

This is just the beginning of how natural language processing is becoming the backbone of numerous technological advancements that influence how we work, learn, and navigate life. But it doesn’t just affect and support digital communications, it’s making an impact on the IT world. Whether you’re considering a career in IT or looking to uplevel your skill set, WGU can support your efforts—and help you learn more about NLP—in a degree program that can fit into your lifestyle. Learn more here.

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