Examples of Natural Language Processing
Hi all,
Natural Language Processing (NLP) allows machines to break down and interpret human language. It’s at the core of tools we use every day from translation software, chatbots, spam filters, and search engines, to grammar correction software, voice assistants, and social media monitoring tools.
These are most common and most powerful uses of natural language processing everyday life.
Online Search engines
Search engines use NLP to surface relevant results based on similar search behaviors or user intent so the average person finds what they need without being a search-term wizard. For example, Google not only predicts what popular searches may apply to your query as you start typing, but it looks at the whole picture and recognizes what you’re trying to say rather than the exact search words.
Smart Assistants
Natural language processing algorithms allow the assistants to be custom-trained by individual users with no additional input, to learn from previous interactions, recall related queries, and connect to other apps. Smart assistants like Apple’s Siri and Amazon’s Alexa recognize patterns in speech thanks to voice recognition, then infer meaning and provide a useful response.
Language Translation
Online translators can translate languages more accurately and present grammatically-correct results. This is infinitely helpful when trying to communicate with someone in another language. Not only that, but when translating from another language to your own, tools now recognize the language based on inputted text and translate it. Google Translate, Microsoft Translator, and Facebook Translation App are examples for translators.
Email filters
Email filters are one of the most basic and initial applications of NLP online. It started out with spam filters, uncovering certain words or phrases that signal a spam message. But filtering has upgraded, just like early adaptations of NLP. Example for newer applications of NLP is found in Gmail’s email classification.
Predictive text
Things like autocorrect, autocomplete, and predictive text are so commonplace on our smartphones that we take them for granted. Autocomplete and predictive text are similar to search engines in that they predict things to say based on what you type, finishing the word or suggesting a relevant one. And autocorrect will sometimes even change words so that the overall message makes more sense. Predictive text, autocorrect, and autocomplete have become so accurate in word processing programs, like MS Word and Google Docs.
Text analytics
Text analysis software, also called text analytics or text mining software, helps users gain insights from both structured and unstructured text data using natural language processing (NLP). Such insights include sentiment analysis, key phrases, language, themes and patterns, and entities, among others. Text analysis tools can consume text data from a variety of sources, including emails, phone transcripts, surveys, customer reviews, and other documents. Examples of text analytics software are RapidMiner, Microsoft Text Analytics API, Google Cloud Natural Language API,IBM Watson Studio.
Thank you!
