30 Sep 2022| Artificial Intelligence
Use Of Natural Language Processing In Our Daily Life
It’s no surprise that teaching AI to “understand” language has taken decades of painstaking, painful work. Our ability to improve Natural Language Processing, or NLP, grows with machine learning skills. As AI and NLP technology improves, it is being used in various ways to make the world a better place. So, first, let’s understand what NLP is.
Natural Language Processing, or NLP, is the software manipulation of language. Language is broken down into parts by the programme during processing to be comprehended and interpreted. Depending on the software, this can be done using speech or text. NLP data sets have risen dramatically when paired with AI and machine learning, allowing the technology to do more and do it better.
The initial prototypes of NLP emerged from linguistics more than 50 years ago. The most prevalent application of NLP technology today can be found in your bag or pocket. NLP and AI are used by intelligent assistants in your home or on your phone to give a voice-driven interface for smart search. Remember that the next time you use Alexa, Siri, Google, Bixby, or any other virtual assistant, you’re using a system that took decades to develop and wouldn’t be conceivable without advanced AI.
NLP, like linguistics, began as a means of gaining a better understanding of language. NLP may be scaled for usage by various businesses as the discipline matures and AI technology develops, making the world a better and more efficient place. NLP and AI applications will continue to grow in scope as AI data handling improves and access to massive quantities of computer power becomes more prevalent. And, when done correctly with a partner familiar with data storage, transformation, and labelling, the technology can assist many individuals.
Here are a few significant examples of how businesses use data science, artificial intelligence, and natural language processing to make the world a better place.
NLP use cases in healthcare are increasing, thanks to piles of undigitized data and handwritten notes. Not only is NLP being used to improve healthcare, but it is also being utilized to save costs. NLP may be used to accomplish rote, repetitive tasks while humans focus on caring for one another, thanks to AI and automation.
Doctors’ notes, clinical trial reports, and patient medical records contain most health data in text format. NLP is now being used to speed up the digitization of paper medical information, allowing for faster and more thorough exchange with patients and other doctors.
Through digital health records, NLP allows for detecting and predicting diseases. After the documents have been digitized, tools like Amazon Comprehend Medical can be used to evaluate them and look for patterns that can help with diagnosis. As a result, diagnoses can be made sooner and with greater accuracy.
NLP and AI tools are in high demand due to their efficiency, efficacy, and ability to lower costs significantly as healthcare expenses rise and the need for mental health treatment increases.
The spread of misleading and inflammatory information has been one of the primary challenges, particularly during the epidemic. Significant differences have resulted from concerns about bias and truth. The MIT NLP Group developed NLP algorithms to assess and decide whether a news source is factual and trustworthy or politically biased to help identify false news. The crew has attempted to improve the software and remove bias that has been encoded into the data analysis over time. While the goal of stopping the spread of fake news is to increase the quality of available information, data scientists have discovered that a lack of knowledge might be harmful.
When it comes to enhancing people’s daily lives, NLP technologies have already proven effective. Smartphones, email clients, and intelligent assistants all use NLP and AI somehow.
NLP technology is used in predictive text, autocorrect, and autocomplete to improve search efficiency and make writing easier. People’s everyday work will be more efficient due to these minor enhancements. Every contact with a well-built autocomplete should teach it something new, so it improves over time.
Searches are no longer literal and rule-based due to a better understanding of intent and extrapolation. Search engines use natural language processing to return the most relevant results to users on the back end. For example, instead of just getting results for which airline flies that flight, you can now get the flight’s current status and arrival or departure information and your actual upcoming flight information if your search engine provider is also your email provider where you have your ticket confirmation.
You’ve probably interacted with NLP and AI customer service technologies if you’ve lately visited a large company’s website and been greeted by a chatbot. These chatbots employ natural language processing and algorithms to interpret and react to client enquiries in real-time.
Sentiment analysis is a technique that allows computers to decipher the emotions behind human words. Sentiment analysis is now possible thanks to recent improvements in NLP. Earlier versions of NLP technology could only understand words, not the feelings associated with them. Organizations can use sentiment analysis to smooth out consumer interactions and prevent more significant issues from arising on social media.
Companies employ natural language processing software in social media and customer care calls to better comprehend client sentiment and teach their software to do the same. When you hear “this call may be recorded for training purposes,” it’s possible that your call is being filtered by NLP software to improve future customer service.
The new Google Assistant technology, which can make phone calls and schedule appointments for users, also employs NLP and sentiment analysis.
The technology is better at understanding what’s being conveyed as NLP is paired with more significant data analysis and machine learning approaches. NLP technology evolves and makes the world better through data labeling and analysis.
Improved customer service, translation services, and health care make the globe a better place to live in and traverse. On the other hand, NLP cannot improve without high-quality annotated training data. With predictive text and intelligent assistants, natural language processing and AI firms collaborate to make the world a more efficient place by using high-quality labeled data.
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