TAILIEUCHUNG - Báo cáo khoa học: "Text-to-text Semantic Similarity for Automatic Short Answer Grading"

In this paper, we explore unsupervised techniques for the task of automatic short answer grading. We compare a number of knowledge-based and corpus-based measures of text similarity, evaluate the effect of domain and size on the corpus-based measures, and also introduce a novel technique to improve the performance of the system by integrating automatic feedback from the student answers. Overall, our system significantly and consistently outperforms other unsupervised methods for short answer grading that have been proposed in the past. . | Text-to-text Semantic Similarity for Automatic Short Answer Grading Michael Mohler and Rada Mihalcea Department of Computer Science University of North Texas mgm0038@ rada@ Abstract In this paper we explore unsupervised techniques for the task of automatic short answer grading. We compare a number of knowledge-based and corpus-based measures of text similarity evaluate the effect of domain and size on the corpus-based measures and also introduce a novel technique to improve the performance of the system by integrating automatic feedback from the student answers. Overall our system significantly and consistently outperforms other unsupervised methods for short answer grading that have been proposed in the past. 1 Introduction One of the most important aspects of the learning process is the assessment of the knowledge acquired by the learner. In a typical examination setting . an exam assignment or quiz this assessment implies an instructor or a grader who provides students with feedback on their answers to questions that are related to the subject matter. There are however certain scenarios such as the large number of worldwide sites with limited teacher availability or the individual or group study sessions done outside of class in which an instructor is not available and yet students need an assessment of their knowledge of the subject. In these instances we often have to turn to computer-assisted assessment. While some forms of computer-assisted assessment do not require sophisticated text understanding . multiple choice or true false questions can be easily graded by a system if the correct solution is available there are also student answers that consist of free text which require an analysis of the text in the answer. Research to date has concentrated on two main subtasks of computer-assisted assessment the grading of essays which is done mainly by checking the style grammaticality and coherence of the essay cf. Higgins et al. 2004 and .

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