Which are the top Common NLP Examples?

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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 the 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.

Also Read: Ethical Challenges in an AI

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