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Paraphrase recognition is used in a number of applications such as tutoring systems, question answering systems, and information retrieval systems. The context of our research is the iSTART reading strategy trainer for science texts, which needs to understand and recognize the trainee’s input and respond appropriately. This paper describes the motivation for paraphrase recognition and develops a definition of the strategy as well as a recognition model for paraphrasing. | iSTART Paraphrase Recognition Chutima Boonthum Computer Science Department Old Dominion University Norfolk VA-23508 USA cboont@cs.odu.edu Abstract Paraphrase recognition is used in a number of applications such as tutoring systems question answering systems and information retrieval systems. The context of our research is the iSTART reading strategy trainer for science texts which needs to understand and recognize the trainee s input and respond appropriately. This paper describes the motivation for paraphrase recognition and develops a definition of the strategy as well as a recognition model for paraphrasing. Lastly we discuss our preliminary implementation and research plan. 1 Introduction A web-based automated reading strategy trainer called iSTART Interactive Strategy Trainer for Active Reading and Thinking adaptively assigns individual students to appropriate reading training programs. It follows the SERT SelfExplanation Reading Training methodology developed by McNamara in press as a way to improve high school students reading ability by teaching them to use active reading strategies in self-explaining difficult texts. Details of the strategies can be found in McNamara in press and of iSTART in Levinstein et al. 2003 During iSTART s practice module the student self-explains a sentence. Then the trainer analyzes the student s explanation and responds. The current system uses simple word- matching algorithms to evaluate the student s input that do not yield results that are sufficiently reliable or accurate. We therefore propose a new system for handling the student s explanation more effectively. Two major tasks of this semantically-based system are to 1 construct an internal representation of sentences and explanations and 2 recognize the reading strategies the student uses beginning with paraphrasing. Construct an Internal Representation We transform the natural language explanation into a representation suitable for later analysis. The Sentence Parser .
