A MACHINE LEARNING APPROACH FOR DESCRIPTIVE ANSWER EVALUATION USING COSINE SIMILARITY
Keywords:
.Abstract
Subjective paper evaluation is a complex and time-consuming task when performed manually. Challenges such as limited understanding and variability in interpreting student responses make automated evaluation using Artificial Intelligence (AI) difficult. Although several approaches have been proposed to assess descriptive answers, many rely on traditional techniques such as keyword matching or word frequency counts, which fail to capture the true semantic meaning of responses. Additionally, the lack of well-curated datasets further restricts the effectiveness of such systems
References
T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient Estimation of Word Representations in Vector Space,” Proceedings of the International
Conference on Learning Representations (ICLR), 2013.
T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, “Distributed Representations of Words and Phrases and their Compositionality,” Advances in Neural Information Processing Systems (NIPS), 2013.
K. W. Church and P. Hanks, “Word Association Norms, Mutual Information, and Lexicography,” Computational Linguistics, vol. 16, no. 1, pp. 22–29,
