article · Engineering and Technology Journal
Knowledge management systems must handle large volumes of unstructured organisational text to assign meaning to institutional data. Natural language processing and text categorization serve as foundational technologies that simplify these systems by automating document sorting, improving information retrieval, and supporting decision-making processes. The development of these methods encompasses early machine learning approaches, such as Naïve Bayes and Support Vector Machines, alongside modern deep learning architectures including Convolutional Neural Networks, Recurrent Neural Networks, and Transformers. Examining real-world industry use cases reveals practical value, while raising critical implementation challenges surrounding system scalability, model explainability, and ethics. Addressing current research gaps highlights substantial opportunities to enhance the overall effectiveness and operational efficiency of knowledge management activities through optimised classification tools.
Organisations generate vast quantities of unstructured written data that are difficult to process manually. Employing advanced natural language processing allows institutions to structure this information, accelerate document retrieval, and make better decisions. Understanding both classical and modern classification models helps organisations identify suitable tools while navigating crucial operational issues like system scalability, transparency, and ethics.
The work addresses industry use cases for enterprises seeking to embed automated document categorisation and information retrieval into knowledge management software. While text classification represents an applied and tested technology in commercial environments, deploying these tools effectively requires resolving operational challenges regarding system scalability, model 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: 10.47191/etj/v11i08.20
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