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Inside SourceFinder: Predicting the Acceptability Status of Candidate Reading-Comprehension Source Documents GRE

Sheehan, Kathleen M.; Kostin, Irene W.; Futagi, Yoko; Hemat, Ramin; Zuckerman, Daniel I.
Publication Year:
Report Number:
ETS Research Report
Document Type:
Page Count:
Subject/Key Words:
SourceFinder, Graduate Record Examination (GRE), Reading Comprehension, Information Sources, Automated Scoring and Natural Language Processing


This paper describes the development, implementation, and evaluation of an automated system for predicting the acceptability status of candidate reading-comprehension stimuli extracted from a database of journal and magazine articles. The system uses a combination of classification and regression techniques to predict the probability that a given document will be deemed acceptable for use in completing a specified passage-creation assignment by at least one test developer. The text features that form the basis of the estimated models are automatically extracted by natural language processing techniques.

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