Exploring Distinct Features for Automatic Short Answer Grading

Abstract

Automatic short answer grading is the study field that addresses the assessment of students’ answers to questions in natural language. The grading of the answers is generally seen as a typical classification supervised learning. To stimulate research in the field, two datasets were publicly released in the SemEval 2013 competition task “Student Response Analysis”. Since then, some works have been developed to improve the results. In this context, the goal of this work is to tackle such task by implementing lessons learned from the literature in an effective way and report results for both datasets and all of its scenarios. The proposed method obtained better results in most scenarios of the competition task and, therefore, higher overall scores when compared to recent works.