article · Engineering and Technology Journal
Knowledge management systems rely on organising vast amounts of unstructured text to extract meaning. Natural language processing, particularly automated text classification, plays a central role in simplifying these systems by categorising documents automatically, improving information retrieval, and supporting organisational decision-making. The field has evolved from early machine learning approaches, including Naïve Bayes and Support Vector Machines, to advanced deep learning architectures such as Convolutional Neural Networks, Recurrent Neural Networks, and Transformers. Alongside these technical advancements, real-world industry use cases reveal critical operational challenges. These include maintaining system scalability, achieving explainability in automated choices, and managing ethical considerations. Addressing these existing research gaps remains essential, as effective natural language processing tools offer substantial capability to increase both the efficiency and overall effectiveness of knowledge management tasks across diverse enterprise environments.
Modern organisations accumulate massive volumes of unstructured text that are difficult to navigate and interpret. Automated text classification methods help transform this unstructured information into searchable, useful assets. By accelerating data discovery and supporting informed decision-making, these technologies can enhance daily workplace productivity, provided that practitioners successfully navigate associated issues around system scale, algorithmic transparency, and ethical use.
This review synthesises text classification methods for enterprise knowledge management, pointing directly to applications in automated document indexing and workplace search tools. Intended users include enterprise software developers and organisations handling large document archives. Because the work reviews existing industry implementations alongside mature machine learning and deep learning models, the underlying technologies appear applied and tested, though prospective adopters must still navigate documented hurdles in scalability and system explainability.
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Knowledge Management Systems (KMS) are required to organize and assign meaning to huge amounts of organizational knowledge that are largely in the form of unstructured text. Natural Language Processing (NLP), and more immediately methods of text categorization, has been one of the principal enabler technologies to enable KMS to be simpler by helping to automatically categorize documents, enhance searching for information, and assist in decision-making. This paper offers an outline of the evolution of NLP-based text classification methods from initial machine learning methods such as Naïve Bayes and Support Vector Machines to current sophisticated deep learning algorithms such as Convolutional Neural Networks, Recurrent Neural Networks, and Transformers. We offer real-world industry use cases, issues of scalability, explainability, and ethics and encapsulate research areas of existing gaps. The findings underscore the enormous potential of NLP text classification to assist the effectiveness and efficiency of knowledge management (KM) activities.
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DOI: 10.5281/zenodo.22156226
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