Natural Language Processing techniques, a subset of Artificial Intelligence, increase their necessity by improving their sub-technologies daily. Language is the prime source of communication and interactions. Without language, communication is not possible, and without communication, process completion is not possible. This is another reason for the increment in the involvement of Natural Language Processing in different domains. The involvement of NLP is also increasing the dimensions of different areas. Some of these domains are -
These domains can be considered use cases of NLP but have separate use cases. The implementation of these use cases can be generalized to an extent. Still, these domains also demand diversities in the different levels of implementation and the expertise required to implement them. Let's briefly understand 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. The business domain consists of some exciting and necessary use cases and problems that the use of Natural Language Processing can address. Some use cases of Natural Language Processing that are used in the Business domain are -
Sentiment Analysis is widely used in social media analytics and web monitoring which allows knowing the customers' insights 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 considers it official to communicate with the help of Email. But this medium is also vulnerable to spamming of the content. Companies that provide Email domains such as Google, Zoho, or Yahoo are researching 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 spam detection task, which is also in sentiment analysis as a pre-processing technique.
These techniques powered by Natural Language Processing allow companies to develop intelligent voice-driven services and interfaces for any product and service. Narrowing the communication gap between machines and human is the most critical and necessary step to increasing the grip on Artificial Intelligence. It can be achieved only by 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 received at any receiving end mainly consists of an unstructured format. The emergence of advanced statistical algorithms resulted in the rise of predictive and prescriptive analytics, making 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 impossible.
What is the role of Natural Language Processing in Healthcare?
Healthcare is the domain where accuracy and efficiency are required because it is directly related to the health of humankind, so the margin of error is approximately near zero. Natural Language Processing addressed healthcare issues with some 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 a better and 24/7 EHR experience. It is referred to ensure unparalleled attention, which is influenced by the demand of completing the documentation work, resulting in customer dissatisfaction with a clinic or hospital. This problem is on the way to becoming an epidemic in healthcare. But it can be prevented using 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 patient-provider interactions, reducing EHR's frustration.
NLP can also reduce the communication and interaction gap between Healthcare technologies (such as patient portals containing patient health records) and patients. The health care domain has already started to adopt the technology in various ways, but the patient still finds it hard to take that.It can be a tool for them. Suppose a system is attached to a Healthcare portal with which patients can interact with their native language. This will result in three advantages.
It will become easy for every patient to understand their 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 the extension of the second point mentioned above. Healthcare reports generally contain parameters that require proper attention. Sometimes human error causes causality, which can be eliminated using the machine (or computer devices, in simple words), which demands the use of Natural Language Processing. A study proved (done in 2018) that using NLP can provide significant relief in the case of calculating the measure of inpatient care and monitoring the clinical guidelines.
Identification of the patients who require Improved Care Coordination
Identification of the patients here refers to correctly identifying the diseases present in any human. In this task, NLP integrated with Machine learning has shown great potential. Some examples are automated Detection of Cancer and Detection of the root causes related to any substance disorder. This process can be done by extracting the information from old existing data sets using NLP and using this information for training Machine Learning and Deep Learning models.
For Example - At Massachusetts General Hospital, the researcher applied techniques to identify the main reasons associated with the social determinants of health.
Natural Language Processing Applications in Finance
The 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 the risk of giving a loan and credit is calculated with 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 solution. Some companies also use Natural Language Processing to mine customers' social media. The data extracted from social media is then used to weigh certain online behaviors. For Example, A Singapore-based company named Lenddo EFL (with 115 employees) developed software called Lenddo Score, which uses machine learning and NLP to assess and calculate an individual's creditworthiness.
Document Search Engine
Natural Language Processing also increased the level of Information extraction from structured and unstructured data, resulting in a big plus in Documentation Processes.
For example, A private firm named 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 is solely based on the text, as every entry and record is maintained in the form of text. With the help of Machine Learning, Natural Language Processing is used to detect fraud and misinterpreted information. Natural Language Processing is generally used to extract and process information that Machine learning models further use to train themselves to expose the fraud.
Natural Language Processing can perform text analytics to search for the appropriate data applications. It can also select the best applications from the available data. Natural Language Processing can be used in different phases and mediums. Some primary use cases used in Recruitment are Information Extraction, Social Media Analytics, Fraud Detection, and Voice Support systems.
Natural Language Processing in Government
Integration of technology and governance is already being used as an electronic governance system. Natural language processing is also part of these systems. Anveshak (The Quester) is an example. It is an information retrieval system based on NLP. On the other hand, however, more initiatives are needed to implement NLP-based governance systems. Because in a multilingual country like India, it can be a game changer. Let's see how to use it for defense research and analysis by the US government. DARPA (Defense Advanced Research Projects Agency) has developed a program called DEFT (Deep Exploration and Filtering of Text) that uses natural language processing to extract relevant information. Unstructured data. This information is further used in analytical procedures to derive insights from the data. The Institute also uses NLP-based models for Strategic Dialogue in the UK to monitor and track signs of radicalization and extremism.
Natural Language Processing in Retail
The retail sector was one of the first to adopt NLP in business, primarily through chatbots and conversational interfaces. The applications of NLP in the retail industry are : The applications in retail are :
Helps understand the needs of users and their intent.
Emulates in-store shopping assistant in e-retail.
Assists in customer support to reduce waiting time.
Help shoppers search for the right product.
What is NLP software?
Natural Language Processing software provides tools for analyzing human language. However, unlike speech recognition software, NLP software can interpret both written and spoken language, making it useful for many applications. It is an excellent automation solution because it can analyze large amounts of data quickly and accurately. It can perform the same analysis on-the-fly as it would require a human to hear and think about the sentence. Perhaps reveal hidden meanings or nuances.
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 -