tailieunhanh - Báo cáo khoa học: "Extracting a Representation from Text for Semantic Analysis"

We present a novel fine-grained semantic representation of text and an approach to constructing it. This representation is largely extractable by today’s technologies and facilitates more detailed semantic analysis. We discuss the requirements driving the representation, suggest how it might be of value in the automated tutoring domain, and provide evidence of its validity. | Extracting a Representation from Text for Semantic Analysis Rodney D. Nielsen1 2 Wayne Ward1 2 James H. Martin1 and Martha Palmer1 1 Center for Computational Language and Education Research University of Colorado Boulder 2 Boulder Language Technologies 2960 Center Green Ct. Boulder CO 80301 Abstract We present a novel fine-grained semantic representation of text and an approach to constructing it. This representation is largely extractable by today s technologies and facilitates more detailed semantic analysis. We discuss the requirements driving the representation suggest how it might be of value in the automated tutoring domain and provide evidence of its validity. 1 Introduction This paper presents a new semantic representation intended to allow more detailed assessment of student responses to questions from an intelligent tutoring system ITS . Assessment within current ITSs generally provides little more than an indication that the student s response expressed the target knowledge or it did not. Furthermore virtually all ITSs are developed in a very domain-specific way with each new question requiring the handcrafting of new semantic extraction frames parsers logic representations or knowledge-based ontologies . Jordan et al. 2004 . This is also true of research in the area of scoring constructed response questions . Leacock 2004 . The goal of the representation described here is to facilitate domain-independent assessment of student responses to questions in the context of a known reference answer and to perform this assessment at a level of detail that will enable more effective ITS dialog. We have two key criteria for this representation 1 it must be at a level that facilitates detailed assessment of the learner s understanding indicating exactly where and in what manner the answer did not meet expectations and 2 the representation and assessment should be learnable by an automated .