Semantic-BERT and semantic-FastText models for education question classification

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AN7YMDF5C" target="_blank" >RIV/00216208:11320/26:N7YMDF5C - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.26555/ijain.v11i2.1955" target="_blank" >http://dx.doi.org/10.26555/ijain.v11i2.1955</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.26555/ijain.v11i2.1955" target="_blank" >10.26555/ijain.v11i2.1955</a>

Result language

angličtina

Original language name

Semantic-BERT and semantic-FastText models for education question classification

Original language description

Question classification (QC) is critical in an educational question-answering (QA) system. However, most existing models suffer from limited semantic accuracy, particularly when dealing with complex or ambiguous education queries. The problem lies in their reliance on surface-level features, such as keyword matching, which hampers their ability to capture deeper syntactic and semantic relationships in the question. This results in misclassification and generic responses that fail to address the specific intent of prospective students. This study addresses this gap by integrating semantic dependency parsing into Semantic-BERT (S-BERT) and Semantic-FastText (S-FastText) to enhance question classification performance. Semantic dependency parsing is applied to structure the semantics of interrogative sentences before classification processing by BERT and FastText. A dataset of 2,173 educational questions covering five question classes (5W1H) is used for training and validation. The model evaluation uses a confusion matrix and K-Fold cross-validation, ensuring robust performance assessment. Experimental results show that both models achieve 100% accuracy, precision, and recall in classifying question sentences, demonstrating their effectiveness in educational question classification. These findings contribute to the development of intelligent educational assistants, paving the way for more efficient and accurate automated question-answering systems in academic environments. © 2025 The Author(s).

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

International Journal of Advances in Intelligent Informatics

ISSN

2442-6571

e-ISSN

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Volume of the periodical

11

Issue of the periodical within the volume

2

Country of publishing house

US - UNITED STATES

Number of pages

17

Pages from-to

210-226

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-105010945883