An overview of Automatic Text Classification
Article Type:
Review Article (دارای رتبه معتبر)

Nowadays, various online resources are growing and disseminating rapidly. In order to organize these resources, attempts have been made to use automatic classification, which often use statistical algorithms and machine learning. Rrecently, attention has been drawn to the use of library classifications. The main challenge here is that classification is an abstract, thought-provoking process, and machine techniques and artificial intelligence have not yet been able to completely replace the human mind. In this paper, we provide an overview of the importance of automatic classification, machine learning and practical algorithms and techniques of clustering and classification like K- nearest neighbor, Bayesian models, artificial neural networks, deep learning, and hybrid classifications. Also, the steps of automatic classification of web pages and the techniques used in each step were mentioned. Achieving a clearer understanding of the automatic classification will enable LIS experts to communication with the experts in the field of artificial intelligence and computers. This could pave the way for interdisciplinary researches.

Journal of Knowledge Retrieval and Semantic Systems, Volume:9 Issue: 32, 2022
191 to 219  
دانلود و مطالعه متن این مقاله با یکی از روشهای زیر امکان پذیر است:
اشتراک شخصی
با عضویت و پرداخت آنلاین حق اشتراک یک‌ساله به مبلغ 990,000ريال می‌توانید 60 عنوان مطلب دانلود کنید!
اشتراک سازمانی
به کتابخانه دانشگاه یا محل کار خود پیشنهاد کنید تا اشتراک سازمانی این پایگاه را برای دسترسی نامحدود همه کاربران به متن مطالب تهیه نمایند!
  • حق عضویت دریافتی صرف حمایت از نشریات عضو و نگهداری، تکمیل و توسعه مگیران می‌شود.
  • پرداخت حق اشتراک و دانلود مقالات اجازه بازنشر آن در سایر رسانه‌های چاپی و دیجیتال را به کاربر نمی‌دهد.
In order to view content subscription is required

Personal subscription
Subscribe for 50 € euros via PayPal and download 60 articles during a year.
Organization subscription
Please contact us to subscribe your university or library for unlimited access!