Publication Type : Journal Article
Thematic Areas : Learning-Technologies, Nanosciences and Molecular Medicine
Publisher : Smart Innovation, Systems and Technologies.
Source : Smart Innovation, Systems and Technologies, Volume 27, Number VOL 1, p.437-443 (2014)
Keywords : AES, Automated essay scoring, Bag of words, Information science, Latent Semantic Analysis, Pre-processing step, Semantics, Singular value decomposition, Syntactic information, Word Sense Disambiguation
Campus : Amritapuri
School : Center for Nanosciences, Department of Computer Science and Engineering, School of Engineering
Center : Technologies & Education (AmritaCREATE), Amrita Center For Research in Analytics, Amrita Center for Nanosciences and Molecular Medicine Move
Department : Nanosciences and Molecular Medicine, Computer Science
Year : 2014
Abstract : The reliability of automated essay scoring (AES) has been the subject of debate among educators. Most systems treat essays as a bag of words and evaluate them based on LSA, LDA or other means. Many also incorporate syntactic information about essays such as the number of spelling mistakes, number of words and so on. Towards this goal, a challenging problem is to correctly understand the semantics of the essay to be evaluated so as to differentiate the intended meaning of terms used in the context of a sentence. We incorporate an unsupervised word sense disambiguation (WSD) algorithm which measures similarity between sentences as a preprocessing step to our existing AES system. We evaluate the enhanced AES model with the Kaggle AES dataset of 1400 pre-scored text answers that were manually scored by two human raters. Based on kappa scores, while both models had weighted kappa scores comparable to the human raters, the model with the WSD outperformed the model without the WSD.
Cite this Research Publication : Prof. Prema Nedungadi and Raj, H., “Unsupervised word sense disambiguation for automatic essay scoring”, Smart Innovation, Systems and Technologies, vol. 27, pp. 437-443, 2014
