article · Engineering and Technology Journal
Organisations rely heavily on Knowledge Management Systems to structure and interpret vast volumes of unstructured text. Natural language processing, particularly through automated text classification, serves as a core enabling technology that streamlines these systems by categorising documents, improving information retrieval, and supporting decision-making processes. The underlying techniques have progressed considerably over time, moving from early machine learning approaches such as Naive Bayes and Support Vector Machines to sophisticated deep learning models including Convolutional Neural Networks, Recurrent Neural Networks, and Transformers. Alongside technical advancements, examining practical industry implementations highlights crucial operational considerations, including system scalability, model explainability, and ethical challenges. Resolving these factors and addressing remaining research gaps is vital to fully realising how text classification can enhance the overall efficiency and effectiveness of knowledge management activities.
Organisations generate enormous volumes of unstructured written information that are difficult to manage and navigate manually. Understanding how text classification tools have evolved helps institutions select the right tools to automatically organise documentation. This improves operational efficiency, accelerates information discovery, and ensures that staff can access relevant data quickly when making key operational decisions.
This work reviews technologies relevant to developers and enterprise software vendors building automated knowledge management tools for corporate and institutional users. The concepts reflect applied and tested technologies currently operating in industry use cases, although deploying them at scale requires organisations to address specific practical barriers related to computational scalability, algorithmic explainability, and ethical considerations.
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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.22156225
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