Using Semantic Networks for Text Classification in Education: “Generating Tailored Questions for Students”
- DOI
- 10.2991/978-94-6463-360-3_5How to use a DOI?
- Keywords
- Semantic networks; Text classification; knowledge representation; Machine learning (ML)
- Abstract
This paper discusses how semantic revolution can be used to represent textual data for text classification purposes. Text classification includes automat- ically classifying text data into predefined classes or categories, such as positive or negative sentiment, or articles categorized into different topics. The paper de- scribes the method of using semantic networks to classify knowledge, including steps such as collecting and preprocessing text data sets, representing text data in the form of semantic networks, and training algorithms. Machine learning on a semantic network, which uses algorithms to classify new textual data and gener- ate questions based on categorical output. The article also includes Python exam- ples for some of the steps involved in the methodology. The paper highlights the power of semantic networks as a tool for knowledge representation and manipu- lation in AI and related fields.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Badr Touis AU - Souhaib Aammou AU - Oussama EL Warraki AU - Jalal Lahiassi PY - 2024 DA - 2024/02/05 TI - Using Semantic Networks for Text Classification in Education: “Generating Tailored Questions for Students” BT - Proceedings of the E-Learning and Smart Engineering Systems (ELSES 2023) PB - Atlantis Press SP - 36 EP - 42 SN - 2667-128X UR - https://doi.org/10.2991/978-94-6463-360-3_5 DO - 10.2991/978-94-6463-360-3_5 ID - Touis2024 ER -