Automatic Difficulty Estimation of Thematic Similarity MultipleChoice Questions, M.Sc. Thesis Sharif University of Technology ; Sameti, Hossein (Supervisor) ; Bokaei, Mohammad Hadi (Supervisor)
Abstract
This project has been conducted in two related phases: In the first phase, we have attempted to write a program capable of answering thematic similarity multiple-choice questions without utilizing any training data. The best performance in this phase was attained by the 25-topic LDA model using the Hellinger distance between the probability distributions of the poetic verses. This model managed to attain an accuracy of 42%, which is very close to the average human performance of 43%. In the second phase, two tasks of seven-class classification and binary classification were defined based on the p-value of the questions. To this end, the questions were initially ranked according to the...
Cataloging briefAutomatic Difficulty Estimation of Thematic Similarity MultipleChoice Questions, M.Sc. Thesis Sharif University of Technology ; Sameti, Hossein (Supervisor) ; Bokaei, Mohammad Hadi (Supervisor)
Abstract
This project has been conducted in two related phases: In the first phase, we have attempted to write a program capable of answering thematic similarity multiple-choice questions without utilizing any training data. The best performance in this phase was attained by the 25-topic LDA model using the Hellinger distance between the probability distributions of the poetic verses. This model managed to attain an accuracy of 42%, which is very close to the average human performance of 43%. In the second phase, two tasks of seven-class classification and binary classification were defined based on the p-value of the questions. To this end, the questions were initially ranked according to the...
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