An important component of knowledge management (KM) is the organization of documents for quick and easy access. One advantageous and effective way of organizing these documents is to group them by a fixed set of specific knowledge categories. For large-scale technical teams, the number of categories can reach thousands or even tens of thousands, which makes the aforementioned cataloging especially useful. Text classification (TC) is a sophisticated process that involves data pre-processing, transformation, dimensionality reduction, application of classification techniques, classifier evaluation, and classifier validation. TC remains a prominent research topic and still depends on human work rather than on machine learning (ML). It is a relatively new area of research and remains in a premature phase. The goal is to develop and evaluate a prototype model that uses ML algorithms to classify technical documentation in a KM system for technical teams of financial institutions involved in software development projects. This research contributes to the field of KM by determining whether an ML approach constitutes a feasible solution for TC in knowledge discovery.
Melnyk, Roman; Snyder, Martha; and Verner, Alexander, "An Analysis of the Effectiveness of Applying a Machine Learning Approach for Classification of Technical Documents in Knowledge Discovery Systems" (2020). AMCIS 2020 TREOs. 58.
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