What Are the Business Applications of NLP? – Fundamentals of Natural Language Processing

What Are the Business Applications of NLP?

Businesses use software and tools for natural language processing (NLP) to make tasks easier, automate them, and streamline them in an accurate way. The following are some examples of use cases:

• Businesses in the insurance, legal, and healthcare industries process and retrieve a high volume of sensitive documents, such as medical records, financial information, and private data.
• Firms apply NLP technology to redact personally identifiable information and protect private information instead of manually evaluating data.
• Chisel AI, for example, uses Amazon Comprehend to extract insurance numbers, expiry dates, and other specific customer information from unstructured text.
• Chat- and voicebots can converse with customers in a more humanlike manner thanks to NLP technologies. Companies use chatbots to expand customer service capability and performance while bringing down overhead expenses. PubNub, a chatbot software developer, utilizes Amazon Comprehend to provide its global customers with regionalized chat functionality. T-Mobile uses NLP to find certain terms in customers’ text messages and offer tailored recommendations. Using machine learning technology, Oklahoma State University has implemented a Q&A chatbot system to respond to inquiries from students.

Amazon Comprehend and Amazon Lex are two examples of natural language processing (NLP) technologies that marketing firms use to get a full picture of how customers see a company’s products and services. They can ascertain the emotions and moods of clients by looking at their comments. For instance, Success KPI provides natural language processing services that let organizations focus on particular areas of sentiment analysis and call analytics in order to get insightful information.

How Does NLP Function?

Natural language processing (NLP) uses a mix of computational linguistics, machine learning, and deep learning models to understand how people talk:

• Computational linguistics is the science of using computers and software technologies to understand and build human language models. For example, researchers use computational linguistics techniques like syntactic and semantic analyses to help robots understand how people talk in conversation. Computational linguistics underpins language translators, text-to-speech synthesizers, and speech recognition software.
• Machine learning is the process of teaching a computer to be more proficient by using example information. Satire, analogies, language structure variations, and grammatical and usage outliers are all aspects of human language that take years to master. Deep learning is a branch of machine learning that trains computers to learn and reason similar to people. It employs a neural network, which consists of computer processing nodes intended to replicate the human brain in terms of data flow.
• Deep learning gives computers the ability to recognize, classify, and equate intricate patterns in incoming data. The initial stage in NLP implementation is often gathering and processing unorganized writing or audio data from sources such as cloud data warehouses, surveys, emails, or internal business process applications.

These techniques are broken down as follows.

Researchers teach NLP models to do specific tasks based on the textual information they have access to by using machine learning and data that has already been processed. Large data samples must be incorporated into the program to improve NLP algorithms’ accuracy. Experts in machine learning then use the model in production or add it to a production environment that already exists. In response to what is put into it, the NLP model can predict a result for the use case it was made for.