Text similarity analysis for evaluation of descriptive answers. (arXiv:2105.02935v1 [cs.LG])

Keeping in mind the necessity of intelligent system in educational sector,
this paper proposes a text analysis based automated approach for automatic
evaluation of the descriptive answers in an examination. In particular, the
research focuses on the use of intelligent concepts of Natural Language
Processing and Data Mining for computer aided examination evaluation system.
The paper present an architecture for fair evaluation of answer sheet. In this
architecture, the examiner creates a sample answer sheet for given sets of
question. By using the concept of text summarization, text semantics and
keywords summarization, the final score for each answer is calculated. The text
similarity model is based on Siamese Manhattan LSTM (MaLSTM). The results of
this research were compared to manually graded assignments and other existing
system. This approach was found to be very efficient in order to be implemented
in an institution or in an university.

Source: https://arxiv.org/abs/2105.02935

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