How Natural Language Processing Is Used
Natural language processing (NLP) 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. Through these examples of natural language processing, you will see how AI-enabled platforms understand data in the same manner as a human, while decoding nuances in language, semantics, and bringing insights to the forefront.
Which are the top 14 Common NLP Examples?
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. Let’s look at 14 common NLP examples in more detail.
1. Online Search Engines
When it comes to examples of natural language processing, search engines are probably the most common. When a user uses a search engine to perform a specific search, the search engine uses an algorithm to not only search web content based on the keywords provided but also the intent of the searcher. In other words, the search engine “understands” what the user is looking for. For example, if a user searches for “apple pricing” the search will return results based on the current prices of Apple computers and not those of the fruit.
2. Email Filters
Email filtering is also one of the common examples of natural language processing. An NLP algorithm is trained on email data and is then able to identify regular, spam, promotion, social media-related emails. Depending on the specific email provider, email filters are also able to identify internal or external emails in an office environment. This makes email filtering a valuable tool to ensure that email users only get the emails they should and, more importantly, serves as an important line of defense against malicious emails which, in turn, protects an organization’s systems against data breaches.
3. Virtual Assistants, Voice Assistants, or Smart Speakers
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. Finally, they use natural language generation (NLG) which gives them the ability to reply and give the user the required response. 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.
4. Semantic Knowledge Management
Semantic knowledge management systems allow organizations to store, classify, and retrieve knowledge that, in turn, helps them improve their processes, collaborate within their teams, and improve understanding of their operations. Here, one of the best NLP examples is where organizations use them to serve content in a knowledge base for customers or users. See how Repustate helped GTD semantically categorize, store, and process their data.
5. Predictive Text and Autocorrect
One of the most prevalent examples of natural language processing is predictive text and autocorrect. NLP ensures that every time a mobile phone user types text on their smartphone, it will suggest what they intended to type. Predictive text and autocorrect are also helpful tools in word processors like Microsoft Word, where they can allow users to work faster and more accurately. People use predictive text from writing formal and effective work emails, to completing an entire thesis - because it just makes things faster than the time taken otherwise. Autocorrects is another ball game. Where they are very helpful in one way, they are notorious for misspelling things because they don’t take into account context, resulting in hilarious fails.
6. Brand Sentiment Monitoring on Social Media
With social media listening, businesses can understand what their customers and others are saying about their brand or products on social media. NLP helps social media sentiment analysis to recognize and understand all types of data including text, videos, images, emojis, hashtags, etc. Through this enriched social media content processing, businesses are able to know how their customers truly feel and what their opinions are. In turn, this allows them to make improvements to their offering to serve their customers better and generate more revenue. Thus making social media listening one of the most important examples of natural language processing for businesses and retailers.
7. Sorting Customer Feedback
NLP can help businesses in customer experience analysis based on certain predefined topics or categories. It’s able to do this through its ability to classify text and add tags or categories to the text based on its content. In this way, organizations can see what aspects of their brand or products are most important to their customers and understand sentiment about their products.
8. Automating Processes in Customer Support
Another one of the crucial NLP examples for businesses is the ability to automate critical customer care processes and eliminate many manual tasks that save customer support agents’ time and allow them to focus on more pressing issues. NLP, for example, allows businesses to automatically classify incoming support queries using text classification and route them to the right department for assistance. This combination of AI in customer experience allows businesses to improve their customer service which, in turn, increases customer retention.
9. Surveillance and Cybersecurity
Apart from allowing businesses to improve their processes and serve their customers better, NLP can also help people, communities, and businesses strengthen their cybersecurity efforts. One of the examples of natural language processing in cyber security includes systems that can help businesses identify vulnerabilities in their systems by analyzing videos through video content analysis, or even conversations on the deep web to find relevant information. Apart from that, NLP helps with identifying phrases and keywords that can denote harm to the general public, and are highly used in public safety management. They also help in areas like child and human trafficking, conspiracy theorists who hamper security details, preventing digital harassment and bullying, and other such areas.
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. They can also be used for providing personalized product recommendations, offering discounts, helping with refunds and return procedures, and many other tasks. Chatbots do all this by recognizing the intent of a user’s query and then presenting the most appropriate response. They use high-accuracy algorithms that are powered by NLP and semantics.
A major benefit of chatbots is that they can provide this service to consumers at all times of the day. More importantly, relevant and personalized content. And in today’s market personalization is the key to success. All this data that is gathered over time is analyzed by NLP algorithms and extracted for emotion mining - all the insights of which can be seen on a sentiment visualization dashboard for an in-depth view of customer sentiment about the brand and product.
11. Automatic Summarization
With automatic summarization, NLP algorithms can summarize the most relevant information from content and create a new, shorter version of the original content. It can do this either by extracting the information and then creating a summary or it can use deep learning techniques to extract the information, paraphrase it and produce a unique version of the original content. Automatic summarization is a lifesaver in scientific research papers, aerospace and missile maintenance works, and other high-efficiency dependent industries that are also high-risk.
12. Machine Translation
With the internet allowing people and businesses to build a truly global presence, machine translation is also one of the NLP examples most important to businesses. This is because machine translation allows them to communicate with people no matter where their customers are or what language they speak. You can learn lyrics to songs you never knew before, read books and websites that are in a different language, access repair and maintenance e-books and pdfs, and do so many cool things without being a pro in a foreign language. Another example of machine translations are translator devices or gadgets that can hear and understand what the other person is saying, and translate it back to you in your preferred language. It’s a must-have piece of technology when you are traveling to a destination that speaks a language you don’t. So much so, that even Amazon added this cool tech to Alexa’s portfolio. All this is possible through NLP.
13. Natural Language Generation
As mentioned earlier, virtual assistants use natural language generation to give users their desired response. It goes further than this, however. NLG can produce all types of text in natural language. As such, it’s one of the most exciting NLP examples. To note, another one of the great examples of natural language processing is GPT-3 which can produce human-like text on almost any topic. The model was trained on a massive dataset and has over 175 billion learning parameters. As a result, it can produce articles, poetry, news reports, and other stories convincingly enough to seem like a human writer created them.
14. Fraud Detection
One of the best NLP examples is found in the insurance industry where NLP is used for fraud detection. Here, it can, for example, be used to detect fraudulent claims. 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.
Explore NLP With Repustate
Natural language processing can be an extremely helpful tool to make businesses more efficient which will help them serve their customers better and generate more revenue. As these examples of natural language processing showed, if you’re looking for a platform to bring NLP advantages to your business, you need a solution that can understand video content analysis, semantics, and sentiment mining. Repustate’s sentiment analyzer offers you deep insights into your customer and employee data and features text analysis, a robust named entity recognition, search inside video, and an extensive visualization dashboard for data insights, and other important functions.