tailieunhanh - Báo cáo khoa học: "Learning to Grade Short Answer Questions using Semantic Similarity Measures and Dependency Graph Alignments"

In this work we address the task of computerassisted assessment of short student answers. We combine several graph alignment features with lexical semantic similarity measures using machine learning techniques and show that the student answers can be more accurately graded than if the semantic measures were used in isolation. We also present a first attempt to align the dependency graphs of the student and the instructor answers in order to make use of a structural component in the automatic grading of student answers. . | Learning to Grade Short Answer Questions using Semantic Similarity Measures and Dependency Graph Alignments Michael Mohler Dept. of Computer Science University of North Texas Denton TX mgm0038@ Razvan Bunescu School of EECS Ohio University Athens Ohio bunescu@ Rada Mihalcea Dept. of Computer Science University of North Texas Denton TX rada@ Abstract In this work we address the task of computer-assisted assessment of short student answers. We combine several graph alignment features with lexical semantic similarity measures using machine learning techniques and show that the student answers can be more accurately graded than if the semantic measures were used in isolation. We also present a first attempt to align the dependency graphs of the student and the instructor answers in order to make use of a structural component in the automatic grading of student answers. 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 classroom assessment . an exam assignment or quiz an instructor or a grader provides students with feedback on their answers to questions related to the subject matter. However in certain scenarios such as a number of sites worldwide with limited teacher availability online learning environments and individual or group study sessions done outside of class an instructor may not be readily available. In these instances students still need some assessment of their knowledge of the subject and so we must turn to computer-assisted assessment CAA . While some forms of CAA 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 made up of free text that may require 752 textual analysis. Research to date has concentrated on two subtasks of CAA grading essay responses which includes checking the style .