Рад у зборнику

објављена

енглески

Olivera Kitanović, Aleksandra Tomašević, Mihailo Škorić, Ranka Stanković, Ljiljana Kolonja

Semantic Textual Similarity of Courses Based on Text Embeddings

Lecture Notes in Networks and Systems

Springer Nature Switzerland

2024

This paper explores the application of textual embeddings to measure semantic similarity between educational courses’ curriculums, aiming to enhance the effectiveness of the next faculty accreditation. Leveraging state-of-the-art natural language processing techniques, we employ pre-trained embeddings to capture the semantic meaning of course descriptions. Our methodology involves transforming course curriculum texts into high-dimensional vector representations, enabling efficient and meaningful comparisons. We evaluate the proposed approach on a diverse dataset of course descriptions, employing established benchmarks for semantic textual similarity . The results demonstrate the effectiveness of our method in capturing nuanced semantic relationships between courses.

311

322

10.1007/978-3-031-71419-1_27

9783031714184

2367-3370

https://link.springer.com/content/pdf/10.1007/978-3-031-71419-1_27

М30

М33

Затворени приступ

All rights reserved

.pdf