Natural Language Processing (NLP) handles natural human language automatically. The natural human language includes speech and text. NLP is performant in a number of fields, which increases daily and can carry out lots of the given tasks. For example:
Recognition and prediction of diseases are done with the help of the medical electronic record of any patient or patient’s speech. In healthcare, NLP is being used to explore from cardiovascular diseases to depression and even schizophrenia. To add, Amazon has a service called Comprehend Medical which uses NLP to extract such information as disease conditions, medications, and treatment outcomes from patient notes, clinical trial reports, and other available sources.
If there is a need to know what customers are saying about this or that organization – NLP might help a lot. It can identify and extract information from various sources, especially social media, about what people think about the product or service. This type of analysis is called a sentiment analysis and it can give a lot of insights about customer preferences and decision-making drivers.
IBM has created a cognitive assistant, which with the help of Artificial intelligence and NLP, learns all the possible information about a person, personalizes the search engine, sends reminders, and even more.
Google and Yahoo are the companies that use NLP to filter and classify emails for people using their mail services. For example, NLP analyses the email content that goes through the server and knows what this message is all about even before it reaches the email inbox. This way, some of the emails go straight into the spam folder.
There is a system developed by MIT NLP Group that determines the accuracy of the source or its political bias. This way, it is possible to understand whether the source can be trustworthy or not.
Alexa (Amazon) or Siri (Apple) have been using NLP for a long time now to answer vocal requests by finding a shop or a song, telling the weather forecast for the asked period, suggest the best route to any institution or even turn on the light at home. For this reason, both Alexa and Siri are called intelligent voice-driven interfaces.
In financial trade, NLP can be used to track different news, reports, and comments about mergers between companies. All of the data people are talking about can be found and put into a trading algorithm. What can you get out of the trading algorithm? Even if the information is a rumor it might turn into big news and selling this news can generate a great profit. So, in the end, NLP gives financial trade an automated process, data enrichment, and the most accurate search and discovery.
Recruitment is a long process from a candidate finding to interviewing. So, applying to NLP might make this process a bit shorter. For example, NLP is used in searching and selecting candidates, identifying the skills of candidates, and acknowledging potential hires even before they become active on the job market. Most of the recruitment tools today offer NLP search that is becoming more accurate.
There is a platform developed by LegalMation that automates daily tasks of litigation. This way legal teams save valuable time, decrease costs for legal processes and focus on what is really important.
In healthcare, NLP can be applied not only to setting diagnoses but also to care delivery and cost reduction if the medical institution is in the process of adopting an electronic health record. NLP can analyze clinical documentation better and optimize various healthcare processes. This is a great chance to focus on better healthcare provision and better patient outcomes.
Alzheimer’s disease is one of the most occurring diseases among the elderly. To improve patient treatment and monitor cognitive impairment through speech such companies as WinterlightLabs help clinical trials and studies to obtain more valuable information. Stanford University has even developed a chatbot that provides therapy for people with central nervous system disorders like anxiety and others.
NLP allows the integration of advanced security techniques by generating questions. For example, data scientists use the algorithm to find additional context for a user’s personal information, extract relevant information using a named entity recognition model, generate questions with the neural network and validate a user’s answer.
Nowadays, NLP may be the best means to effective support in any field but it still has to develop and progress.