Intelligent Assistants Using Natural Language Processing for Hyperautomation

Abstract

Businesses in several industries are attempting to take advantage of the digital revolution that has altered business as people know it in order to enhance production and their efficiency. Hyperautomation is one of the key factors influencing future business efficiency and economic relevance. With the aid of advanced methodologies like artificial intelligence (AI), robotic process automation (RPA), natural language processing (NLP), and machine learning (ML), hyperautomation is a true data-driven digitalization for the next generation industries. Improved productivity, cost-effective automation, and amplified security are some of the major benefits of hyperautomation. Hyperautomation broadens the definition of automation and frees up the workforce to manage tasks that need creativity and the capacity to foresee the future. The subfield of AI called “natural language processing” (NLP) aspires to make it possible for computers to understand spoken and written language similarly to humans. These technologies aid in enhancing the caliber of customer interactions in addition to the consequences of automation. Since there may be thousands of overlapping domains for natural language understanding in intelligent personal digital assistants (IPDAs), a common real-world application with spoken language processing capabilities, the task of determining the best domain to handle an utterance becomes a difficult problem on a large scale. It combines computational linguistics, which models human language using rules, with statistical ML and deep learning models. Also, sentiment analysis, text extraction, machine translation, conversational AI, document AI, and text summarization are all based on NLP. With the rising usage in the financial, insurance, and healthcare sectors, the NLP market as a whole is expanding quickly. Natural language processing has very good prospects because new developments will improve user experience and open up new markets. The main causes of issues are traits such data complexity, sparsity, diversity, dimensionality, and the dynamic properties of the datasets. This chapter emphasizes the necessity of natural language process-based hyperautomation for process automation and technology of the next generation, as well as the current methods and those still being researched. It also identifies the hurdles or issues that still need to be resolved.