Authors:
Tsegaye Misikir Tashu
and
Tomáš Horváth
Affiliation:
Eötvös Loránd University, Hungary
Keyword(s):
Automatic Essay Scoring, Word Mover’s Distance, Semantic Analysis.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence and Decision Support Systems
;
Computer-Supported Education
;
e-Learning
;
Enterprise Information Systems
;
Information Technologies Supporting Learning
;
Intelligent Tutoring Systems
;
Learning Analytics
;
Learning/Teaching Methodologies and Assessment
Abstract:
Automated essay evaluation (AEE) represents not only as a tool to assess evaluate and score essays, but also helps to save time, effort and money without lowering the quality of goals and objectives of educational assessment. Even if the field has been developing since the 1960s and various algorithms and approaches have been proposed to implement AEE systems, most of the existing solutions give much more focus on syntax, vocabulary and shallow content measurements and only vaguely understand the semantics and context of the essay. To address the issue with semantics and context, we propose pair-wise semantic similarity essay evaluation by using the Word Mover’s Distance. This method relies on Neural Word Embedding to measure the similarity between words. To be able to measure the performance of AEE, a qualitative accuracy measure based on pairwise ranking is proposed in this paper. The experimental results show that the AEE approach using Word Mover’s distance achieve higher level o
f accuracy as compared to others baselines.
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