Hybrid Semantic Evaluation of Student Answers Using Rule Matching and BERT Embeddings

Accurate evaluation of student answers in online and traditional assessments is critical in education. In recent years, various text similarity-based methods have been proposed. However, there are certain challenges, such as the semantic and structural understanding of text. Thus, this paper uses the BERT model to present a hybrid evaluation framework that combines rule-based similarity techniques with deep semantic knowledge. The rule-based component utilizes predefined linguistic and domain-sp

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