What are some natural language processing examples? Read on to learn about several general and specific applications.
Natural Language Processing or NLP is a sub-branch of Artificial Intelligence (AI) that uses linguistics and computer science to make natural human language understandable to machines. Systems with NLP capability can use algorithms and machine learning to analyze, interpret, and extract meaning from written text or speech.
Natural language processing systems analyze a range of aspects relating to text and language, such as word usage (syntax), meaning (semantics), pragmatics or the study of language use, and the study of rules for forming admissible words (morphology). Computer science techniques can then transform these observations into rules-based machine learning algorithms capable of performing specific tasks or solving particular problems.
For businesses and institutions, the large-scale analysis of massive volumes of unstructured data in text form and spoken audio enables machines to make sense of a world of information that might otherwise be missed. NLP (Natural Language Processing) examples cover fields as diverse as customer relations, social media, current event reporting, and online reviews.
Natural language processing mechanisms and tools make it possible for machines to sift through information and reroute it with little or no human intervention, allowing for the real-time automation of various processes. And by adapting them to the specific characteristics of a given sub-language or technical vocabulary, NLP tools can be custom-tailored to the needs of virtually any industry.
In this article, we’ll be looking at several natural language processing examples — ranging from general applications to specific products or services.
NLP Example – Email Filtering
This is one of the longest-running natural language processing examples in action. Among the first uses of natural language processing in the email sphere was spam filtering. Systems flag incoming messages for specific keywords or topics that typically flag them as unsolicited advertising, junk mail, or phishing and social engineering entrapment attempts.
More recently, the popular web platform Gmail has been using NLP to classify messages into promotion, Social, or important categories. Again, keywords and phrases in the message text form the basis of comparison enabling natural language processing algorithms to sort through incoming mail.
NLP Example – Search Engines
Online search engines are another natural language processing example. Search engines use natural language processing to throw up relevant results based on the perceived intent of the user, or similar searches conducted in the past.
Interpretive analysis enables the NLP algorithms on Google to recognize early on what you’re trying to say, rather than the exact words you use in the search. This allows the platform to predict which popular searches might apply to your query as you begin to type and display auxiliary tools that are probably relevant to your inquiry, such as a calculator if you enter a mathematical expression or formula.
Klevu is a self-learning smart search provider for the eCommerce sector, powered by NLP. The system learns by observing how shoppers interact with the search function on a store website or portal. Klevu automatically adds contextually relevant synonyms to a given catalog. This approach can triple the depth of the resulting search output. The software also allows for a personalized experience, offering trending products or goods that a customer previously searched.
NLP Example – Predictive Text Tools
Users of productivity applications ranging from word processors to text entry boxes on a smartphone will doubtless be familiar with features such as autocorrect, which amends text as you’re typing or dictating it.
Autocomplete and predictive text are other tools in this class that use Natural Language Processing techniques to predict word or sentence output as you’re entering the data. Sophisticated systems can even alter words so that the overall structure of the output text reads better and makes more sense.
NLP Example – Spell Checking
Spell-checking is another natural language processing example. This is another NLP-powered feature that’s been around for a while in word processors and other office productivity software. It’s now integrated with many forms of text entry, including mobile phones. Some tools can check your spelling on the fly as you type, and more basic implementations run a spell check after you finish. Some systems even offer a range of synonyms for the words you use.
In the business realm, the Customer Relationship Management (CRM) platform Salesforce includes a spell checker in its contact form so that the system can clean up the text of customer requests for help desk support or contact with a sales rep.
NLP Example – Machine Language Translation
Machine translation enables the automatic conversion of text in one language to equivalent text in another language that retains the same meaning. Early systems relied on dictionary and vocabulary rules and often returned stilted output that did not conform with the idiomatic rules of the target output language.
However, with the availability of big language data and the evolution of neural networks, today’s translation systems can produce much more idiomatically correct output in real or near real-time. This provides a distinct advantage for those needing to deal with customers or contacts in different countries.
For example, two former Google Translate engineers developed the Lilt translation tool and can integrate with third-party business platforms such as customer support software. The system uses interaction with a human translator to learn its language idioms and improve and enhance its performance over time.
NLP Example – Chatbots
Much of the question and answer or customer support activity on corporate websites now occurs through chatbots. For Frequently Asked Questions and other knowledge bases, some of the more basic implementations rely on a set of pre-programmed rules and automated responses. However, more sophisticated chatbots use Natural Language Processing to interpret input from consumers or users and generate their text or spoken output.
On Facebook, for example, Messenger bots are enabling businesses to connect with their clients via social media. Rather than straight advertising, these chatbots interact directly with consumers and can provide a more engaging and personalized experience.
NLP Example – Smart Assistants
Consumer-level gadgets incorporating voice assistants like Amazon Alexa or Apple’s Siri have become commonplace. As natural language processing examples, digital assistants use natural language processing to interpret commands and observations from the user, generate appropriate replies, and provide relevant responses to queries and instructions. Machine learning is typically an integral part of these systems, which learn from their encounters with users and improve the accuracy of their responses over time.
There are commercial applications for digital assistants, as well. For example, since 2016, Mastercard has been using a virtual assistant that provides users with an overview of their spending habits and deeper insights into what they can and cannot do with their credit or debit card.
Alexa Skills
Following on from this, Amazon Alexa offers organizations the chance to create unique tools and features for their consumers through its Skills ecosystem. Companies can use natural language processing and other technologies to create applications (skills) that integrate with their products and services. Each month, there are financial rewards to the developer who makes the most engaging skill in several different categories.
Sentiment Analysis
Natural Language Processing algorithms designed to identify the human emotions associated with given keywords and sentence structures are the basis for sentiment analysis. Using this technique, organizations can gauge customer opinion about their products, customer care, and services and assess the tone of discussion and commentary on social media and online forums.
For example, Sprout Social is a social media listening tool for monitoring and analyzing the activity and discourse concerning a particular brand.
Natural Language Generation (NLG)
Natural Language Generation or NLG is a sub-branch of NLP that focuses on developing applications and systems capable of producing written or spoken output text in natural language, based on input that summarizes the meaning of the anticipated output. The GPT-2 text-generation system released by Open AI in 2019 uses NLG to produce stories, news articles, and poems based on text input from eight million web pages.
NLG has applications ranging from the summarization of a body of text to answering questions from the user. Chatbots with natural language output can provide a more human-like response, providing a more engaging experience to consumers and customer support.
