NATURAL LANGUAGE PROCESSING FOR AUTOMATED IT SERVICE DESK RESOLUTION

Abstract

By automating the process of answering user inquiries, Natural Language Processing (NLP) has become a game-changing technology for improving IT service desk operations. Organizations may discover, categorize, and address problems with little human involvement by utilizing breakthroughs in natural language processing (NLP) to extract semantic meaning from unstructured data. NLP's ability to handle big datasets and produce insightful results was shown in early research on its applications in radiology. Methods like ontology-based systems and rhetorical structure theory have further made it easier to create strong frameworks for comprehending and processing textual data in a variety of fields, including IT service management. Examples of how NLP has improved system efficiency and user pleasure include automatic picture analysis, security policy extraction and semantic interoperability in medical applications. While deep NLP techniques allow for precise classification and resolution of complicated queries, shallow knowledge-based approaches have been used in the IT sphere to improve early-stage problem understanding. NLP capabilities are extended by contemporary spoken language systems and historical text processing, which allow for contextual understanding and real-time query handling in dynamic situations. By combining NLP with semantic web technologies, IT service desks have been able to develop into flexible systems that can automatically resolve tickets, greatly speeding up response times and improving accuracy. The many uses of natural language processing (NLP) for automated IT service desk resolution are examined in this study, with particular attention paid to approaches like data structure discovery, semantic disambiguation, and cross-disciplinary developments. Building on findings from software engineering and clinical research, it suggests a paradigm that combines information extraction and decision-making algorithms to enhance the effectiveness of IT service desks and user experience. Future advancements in this crucial area could lead to more innovation, as seen by improved multimodal NLP systems.

Keywords: Natural Language Processing (NLP), Automated IT Service Desk, Semantic Understanding, Contextual Query Processing, Adaptive Learning, Tokenization