Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/138207
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dc.titleROBUST TRAIT-SPECIFIC ESSAY SCORING USING NEURAL NETWORKS AND DENSITY ESTIMATORS
dc.contributor.authorKAVEH TAGHIPOUR
dc.date.accessioned2017-12-31T18:01:35Z
dc.date.available2017-12-31T18:01:35Z
dc.date.issued2017-03-24
dc.identifier.citationKAVEH TAGHIPOUR (2017-03-24). ROBUST TRAIT-SPECIFIC ESSAY SCORING USING NEURAL NETWORKS AND DENSITY ESTIMATORS. ScholarBank@NUS Repository.
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/138207
dc.description.abstractWe have proposed a novel approach to automated essay scoring based on recurrent and convolutional neural networks. Unlike existing systems, our approach does not rely on manually-engineered features and learns features from data. The experiments show that our approach outperforms state-of-the-art automated essay scoring systems and can be used for modeling various essay scoring traits, such as argument strength and essay organization. We have also proposed a novel method based on density estimators to identify and penalize fake (computer-generated) essays. Unlike existing methods, our module does not need fake essays in the training data. This module maps the essays into an N-dimensional feature space and uses a simple decision rule to detect fake essays. We have shown that our module is able to identify fake essays generated based on N-gram language models and context-free grammars.
dc.language.isoen
dc.subjectautomated essay scoring, recurrent and convolutional neural networks, argument strength, essay organization, fake essay detection, density estimation
dc.typeThesis
dc.contributor.departmentCOMPUTER SCIENCE
dc.contributor.supervisorNG HWEE TOU
dc.description.degreePh.D
dc.description.degreeconferredDOCTOR OF PHILOSOPHY
Appears in Collections:Ph.D Theses (Open)

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