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 .

• Báo cáo khoa học
• Text to text Semantic Similarity
• Automatic Short Answer Grading
• Michael Mohler and Rada Mihalcea
• scientific reports
• model language
• process natural language
• Dialog Control
• Natural Language System
• Dialog Control in a Natural Language System
• Expanding the Horizons
• Natural Language Interfaces
• Phil Hayes
• PROSPECTS OR PRACTICAL
• NATURAL LANGUAGE SYSTEMS
• Workshops
• NATURAL LANGUAGE
• INTERACTION WITH MACHINES
• Long Papers
• COMPUTER INTEBFACE DESIGN MURRAY TUROFF DEPARTMENT OF COMPUTER
• IiVFORMATION SCIENCE IIEW JERSEY INSTITUTE OF TECHNOLOGY
• EVALUATION OF NATURAL LANGUAGE
• INTERFACES TO DATABASE SYSTEMS
• A PANEL DISCUSSION
• database query system
• J
• Norwood Crout
• INTERFACES TO DATA BASE SYSTEMS
• Bozena Henisz
• INTERPRETING NATURAL LANGUAGE
• DATABASE UPDATES
• S
• Jermld Kaplan Jim David
• TRANSPORTABLE NATURAL LANGUAGE
• INTERFACES TO DATABASES
• Gary G
• Hendrlx and William H
• Lewis
• DATABASES THEORETICAL
• TECHNICAL ISSUES
• TRANSPORTABLE NATURAL LANGUAGE INTERFACES
• PROBLEMS AND TECHNIQUES
• Barbara J
• Grosz
• THEORETICAL TECHNICAL ISSUES
• NATURAL LANGUAGE ACCESS TO DATABASES
• R
• Petrick
• Domain Independent
• Database Access Systems
• ISSUES IN NATURAL LANGUAGE
• ACCESS TO DATABASES
• A LOGIC PROGRAMMING PERSPECTIVE
• Sharon C
• Salveter David Maier
• PLANNING NATURAL LANGUAGE
• EXPRESSIONS REFERRING
• Douglas E
• Appelt
• THE TEXT SYSTEM
• NATURAL LANGUAGE GENERATION
• Kathleen R
• M
• MENTING A DATABASE
• KNOWLEDGE REPRESENTATION
• THE KEY TO THE SELECTION PROBLEM
• E
• Jeffrey Conklin David D
• McDonald
• A KNOWLEDGE ENGINEERING
• NATURAL LANGUAGE UNDERSTANDING
• Stuart C
• Shapiro
• NATURAL LANGUAGE TEXTS
• NECESSARILY GRAMMATICAL
• EVEN COMPLETE
• UNGRAMHATICALITY
• EXTRA GRAMMATICALITY
• NATURAL LANGUAGE UNDERSTANDING SYSTEMS
• Taxonomy
• Descriptions
• Individuals in Natural
• Language Understanding
• The Use of Ooject Special Knowledge
• Natural Language Processing
• Mark H
• Bursteln