Text similarity analysis for evaluation of descriptive answers
This addresses the need for fair and automated evaluation in educational settings, though it is incremental as it applies existing NLP methods to a specific domain.
The paper tackles automated grading of descriptive exam answers using a text similarity model based on Siamese Manhattan LSTM, achieving results comparable to manual grading and existing systems, making it efficient for institutional implementation.
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.