Short Answers Assessment Approach based on Semantic Network

Abstract

      Finding similarities in texts is important in many areas such as information retrieval, automated article scoring, and short answer categorization. Evaluating short answers is not an easy task due to differences in natural language. Methods for calculating the similarity between texts depend on semantic or grammatical aspects. This paper discusses a method for evaluating short answers using semantic networks to represent the typical (correct) answer and students' answers. The semantic network of nodes and relationships represents the text (answers). Moreover, grammatical aspects are found  by measuring the similarity of parts of speech between the answers. In addition, finding hierarchical relationships between nodes in networks. The similarity is then calculated, and students' answers are evaluated. The best results are for weights (α = 0.1, = 0.6, γ = 0.3) = 1.82 from 5, giving more weight in nodes similarity by 6, least similarity by relationships by 3, and least similarity by parts of speech by 1.