In an era where artificial intelligence is the crux of every leading technology, human interaction with computers is abundant. NLP is a subset of AI that programs computers to analyze and process large quantities of data in natural language to enable seamless human-computer interaction. For businesses that deal with a large amount of unstructured data, in the forms of emails, social media, chats, surveys, etc. Natural Language Processing tools are an asset. Workplaces can gain valuable insights that would help derive crucial business decisions and automate tasks. Let’s take a look at 7 cases in which NLP can help businesses.
Primarily, NLP has two use cases. 1. Comprehend human speech and extract the meaning. 2. Unlocking unstructured data in databases & documents by mapping out essential concepts and values and allowing physicians to use this information for decision-making and analytics. In a report by Chilmark Research, the company has outlined the following use cases across three stages of maturity when it comes to use cases:
Mainstay use cases with a proven ROI: speech recognition, clinical documentation improvement, data mining research, computer-assisted coding, automated registry reporting.
Emerging use cases that will have an immediate impact: clinical trial matching, clinical decision support, risk adjustment, and hierarchical condition categories.
Next-gen use cases are on the horizon: ambient virtual scribe, computational phenotyping, and biomarker discovery, population surveillance.
Data mining integration in health IT systems allows healthcare providers and hospitals to reduce subjectivity in decision-making and provide new useful medical knowledge. Predictive models used in medicine provide the best knowledge support ever and help develop a set of clear and reliable predictions for doctors to make better, informed decisions and improve prognosis, diagnosis, and patient treatment. Healthcare centers can leverage NLP by improving patient interactions with the provider and the EHR. It will help increase awareness amongst patients and improve care quality.
NLP-based chatbots are smart to understand the language semantics, text structures, and speech phrases, enabling them to analyze a vast amount of unstructured data and comprehend it. NLP is capable of understanding expressions across languages which makes a bot more capable of understanding different nuances. Over time these chatbots gain the ability to interpret abbreviations and slang. Not only can they converse with humans but also understand human emotions and behavior, providing a more personalized experience to the customers. In operations like customer service or invoice processing, human resources can be reduced to a great extent with NLP-based chatbots. It helps increase the overall productivity, employee efficiency and cut down the human dependency.
A business must stay updated with the fast-changing market trends and standards. NLP is a vital tool to track and monitor the intelligence reports for extracting information for businesses, to formulate future strategies. Currently, in the financial sector, NLP is used to determine the market situation, employee changes, tender-related information, extracting information from large repositories. In the future, it is expected that NLP would become an integral part of several business domains.
Employee selection is a crucial aspect of any organization. The HR department is encumbered with enormous amounts of data on potential candidates, making the processing time-consuming. The filtering and shortlisting of such huge data result in a cumbersome situation. With NLP this process can be simplified. HR professionals can extract the necessary information such as names, skills, locations, and educational backgrounds of candidates with automated tools for candidate sourcing that would scan resumes for the set parameters. This not only fastens the process but also ensures that the deserving candidates are selected.
Modern machine translators have surpassed the traditional rule-based language translators, which can translate millions of words. The manual way of translating from one language to another was a cumbersome process and time-consuming. Businesses and individuals can now leverage these advanced tools to interact globally, hassle-free, and form a ubiquitous nexus. A foreign language is no longer a barrier. Communication is more instantaneous and crisper, making networking a seamless process.
Financial institutions such as banks and insurance companies face the challenge of gauging the risk involved in lending loans to individuals due to a lack of credit history. NLP can evaluate the creditworthiness of a customer, along with estimating the risks associated with lending. NLP algorithms analyze the digital footprint of the potential borrower across social media, browsing history, geolocation data, and relevant information of personal devices. By analyzing this data, the software predicts a credit score that derives the worthiness of the customer. The scrutiny is done with the customer’s permission without any invasion of privacy and is kept confidential, with no access to any third party.
Sentiment analysis is the process of detecting positive or negative sentiment in text. It is often used by businesses to detect sentiment in social data, gauge brand reputation, and understand customers. Since customers express their thoughts and feelings more openly than ever before, sentiment analysis is becoming an essential tool to monitor and understand that sentiment. Automatically analyzing customer feedback, such as opinions in survey responses and social media conversations, allows brands to learn what makes customers happy or frustrated so that they can tailor products and services to meet their customers’ needs. Sentiment analysis helps businesses process huge amounts of data efficiently and cost-effectively. With its real-time analysis, businesses can provide quick solutions to ongoing problems, such as catering to customer feedback on social media.
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