Natural Language Processing techniques, a subset of Artificial Intelligence increasing its necessity with the improvement of its sub-technologies day by day. Language is the prime source of communications and interactions. Without language, communication is not possible, and without communication, process completion is not possible. This is also another reason for the increment in the involvement of Natural Language Processing in different domains. The involvement of Natural Language Processing (NLP) is also increasing the dimensions of different areas. Some of these domains are -
These domains can be considered as use cases of Natural Language Processing but also have their separate use cases. The implementation of these use cases can be generalized to an extent, But these domains also demand diversities in the different level of implementation as well as in the expertise required to implement them. Let’s understand briefly about these domains from the mirror of Natural Language Processing. One point is to be noted here that Natural Language Processing also requires some integration with other technologies such as Machine Learning, Deep Learning, and Big Data Analytics.
Let’s start with the domain of commercialization. It is pretty evident that the business domain is consist of some interesting and necessary use cases and problems which can be addressed by the use of Natural Language Processing. Some use cases of Natural Language Processing which are used in the Business domain are -
It is widely used in social media analytics and web monitoring which allow knowing the insights of the customers concerning particular products or services. It can be advantageous for any company to know about the thinking of the customers about a product so that they can know about the scope of improvement and how to achieve robustness. Natural language processing can not solely handle this task; it requires integration with highly computational methods such as Machine learning and deep learning to do the back end computation and Big data analytics to digest the data at an enormous scale.
Emails are adopted as a medium of communication officially now. Even the government consider it is official to communicate with the help of Email. But this medium is also vulnerable to spamming of the content. Companies which provide Email domains such as Google, Zoho or Yahoo are researching in the field of making it Full-proof by using different measures. Email filtering is an everyday use case of Natural Language Processing by applying various text analytics measures. It is a task of spam detection which is also in sentiment analysis as a pre-processing technique.
These are techniques which are powered by Natural Language Processing that allow companies to develop smart voice-driven services and interfaces for any product and service. To narrow the communication gap between the machines and human is the most critical and necessary step to increase the grip on Artificial Intelligence. It can be achieved by only and only Voice Recognition which is possible by Natural Language Understanding a sub-process of Natural Language Processing.
Information is the new fuel, it is a well-known fact now. But the data which is received at any receiving end mostly consist of unstructured format. The emergence of the advanced statistical algorithms results in the rise predictive analytics and prescriptive analytics which made the prediction system more accurate. But these algorithms demand more and more information for finding the patterns and Matching them. Of course, Machine learning and Deep learning methods are doing an impressively great job, but without Natural Language Processing these things are not possible.
What is the role of Natural Language Processing in Healthcare?
Healthcare is the domain where accuracy and efficiency both are required because it is directly related to the health of the human kind, so the margin of error is approximately near to zero. Natural Language Processing addressed the issues of healthcare with some of its application and use cases.
Raising the bar of provider interactions with Patients and EHR The main concern and priority in nowadays the healthcare system is to provide better and 24/7 EHR experience. It is referred to ensure unparalleled attention which also gets influenced by the demand of completing the documentation work, results in the dissatisfaction of the customer toward a clinic or hospital. This problem is on the way of becoming an epidemic for healthcare. But it can be prevented with the use of NLP which can be used mainly in three ways.
Intelligent Voice Support Systems
For Example - In Pennsylvania, Well Span Health started using voice-based tools for dictating the patient-provider interactions which reduced the frustration of EHR.
Raising the bar of patient health literacy
NLP can also be used to reduce the communication and interaction gap between Healthcare technologies (such as patient portals which contain health records of a patient) and patients. Health care domain already started to adopt the technology in various ways, but the patient still finds it hard to take that. NLP can be a tool for them. Suppose a system attached with a Healthcare portal with which a patient can interact with his/her native language. This will result in three advantages.
It will become easy for every patient to understand his/her health status.
It reduces the chances of Human error in the system.
It will give a comfortable space to doctors too.
Increasing the dimension of high quality of care
This point can be considered as the extension of the second point mentioned above. Healthcare reports generally contain parameters which require proper attention. Sometimes human error cause causality which can be eliminated using the machine (or computer devices, in simple words) which demand the use of Natural Language Processing. A study proved (done in 2018) the use of NLP can provide significant relief in the case of calculating the measure of inpatient care and monitoring the clinical guidelines.
Identification of the patients which require Improved Care Coordination
Identification of the patients here refers to the proper identification of the diseases present in any human. In this task NLP integrated with Machine learning have shown great potential. Automated Detection of Cancer, Detection of the root causes related to any substance disorder are some of the examples. This process can be done by extracting the information from old existed data set using NLP and using this information for training Machine Learning and Deep Learning models.
For Example - At Massachusetts General Hospital, the researcher used applied NLP techniques to identify the main reasons associated with the social determinants of health.
Natural Language Processing Applications in Finance
The application of NLP application in finance:
Credit Scoring Model/Method
Credit scoring is a risk estimation method (estimate risk while providing the loan to any party) in which risk of giving loan and credit is calculated by the help of the credit score against the credit histories of the potential borrowers. Natural Language Processing (integrated with AI) related to credit scoring more often than not are predictive analytics solutions. Natural Language Processing is also used by some companies to mine the social media of the customers. The data extracted from social media is then used to likely weighs certain online behaviors. For Example, A Singapore-based company named as Lenddo EFL (with 115 employees) developed software called Lenddo Score which use machine learning and NLP to assess and calculate an individual’s creditworthiness.
Document Search Engine
Natural Language Processing also increased the level of the Information extraction from structured and unstructured data which resulted in a big plus in the field of Documentation Processes.
For example, A private firm named as Nuance Communications based in Massachusetts developed software known as Nuance Document Finance Solution, which is used to aid financial services companies in automatizing the documentation process.
Fraud Detection in Banking
The world of finance is very vulnerable to fraud and this world solely based on the text as every entry and record is maintained in the form of text. Natural Language Processing with the help of Machine Learning is used to detect fraud and misinterpreted information. Natural Language Processing generally used to extract information and process information which is further used by Machine learning model to train themselves to expose the fraud.
USA ‘s Defence Research and Analysis wing DARPA (Defence Advanced Research Projects Agency) developed a program DEFT (Deep Exploration and Filtering of Text) in which Natural Language Processing is used to extract pertinent information from unstructured data. This information is further used to analyttics procedures to draw some insights from the data.Moreover, NLP based models also used by the Institute for Strategic Dialogue in the United Kingdom to observe and trace the signs of radicalization and extremism.
Natural Language Processing in Recruitment
Natural Language Processing can be used to perform the text analytics for searching the appropriate applications from the data, and it also can be used for selecting the best applications from the data available. Natural Language Processing can be used on different phases and with a different medium. Some of the primary use cases which can be used in Recruitment are Information Extraction, Social Media Analytics, Fraud Detection, and Voice Support systems.
A Comprehensive Approach
Natural Language Processing based Solutions for Sentiment Analysis, Text Categorizations and Information Extraction for Enterprises.To know more about leveraging Natural Language Processing for growing your business we recommend taking the following steps -