Research Article Open Access

Customized Named Entity Recognition Using Bert for the Social Learning Management System Platform CourseNetworking

Kayal Padmanandam1, KVN Sunitha2, Behafarid Mohammad Jafari3, Ali Jafari4, Mengyuan Zhao5 and Nikitha Pitla1
  • 1 Department of Information Technology, BVRIT HYDERABAD College of Engineering for Women, Hyderabad, India
  • 2 Department of Computer Science and Engineering, BVRIT HYDERABAD College of Engineering for Women, Hyderabad, India
  • 3 Department of Electrical and Computer Engineering, Indiana University-Purdue University Indianapolis, Indianapolis, United States
  • 4 Department of Computer and Information Technology, Indiana University-Purdue University Indianapolis, Indianapolis, United States
  • 5 Department of Instructional System Technology, School of Education, Indiana University, Bloomington, United States

Abstract

Named Entity Recognition (NER) is an information extraction task and one of the most researched applications to extract knowledge from massive data. Conventional NER systems identify predefined entities like name, person, location, organization, time, etc. However, there is a limitation to identifying user-defined entities that are specific to an application. This challenge introduces the concept of customized NER. For instance, if a learning management system like CourseNetworking (CN) needs to identify the skill set of a user from their posts, the existing pre-trained NER models cannot be used. To overcome this information extraction limitation, we propose a customized named entity recognition system for the CN platform using the deep learning model, Bi-Directional Encoder Representation from Transformer (BERT) which is a transformer-based deep learning technique where all output elements are connected to all input elements with dynamic weight connections. The proposed customization model can be employed to train any entity of user choice with a decent amount of training dataset. The model shows 70-72% recall and F1-Score varied on the number of epochs trained. This model is used in various applications like fraudulent detection, recommendation systems, and business intelligence.

Journal of Computer Science
Volume 20 No. 1, 2024, 88-95

DOI: https://doi.org/10.3844/jcssp.2024.88.95

Submitted On: 8 February 2023 Published On: 15 December 2023

How to Cite: Padmanandam, K., Sunitha, K., Jafari, B. M., Jafari, A., Zhao, M. & Pitla, N. (2024). Customized Named Entity Recognition Using Bert for the Social Learning Management System Platform CourseNetworking. Journal of Computer Science, 20(1), 88-95. https://doi.org/10.3844/jcssp.2024.88.95

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Keywords

  • Customized Named Entity Recognition
  • Named Entity Recognition (NER)
  • Information Extraction
  • BERT
  • Simple Transformers
  • CourseNetworking