Paper: Automated Scoring Using A Hybrid Feature Identification Technique

ACL ID C98-1032
Title Automated Scoring Using A Hybrid Feature Identification Technique
Venue International Conference on Computational Linguistics
Session Main Conference
Year 1998
Authors

This study exploits statistical redundancy inherent in natural language to automatically predict scores for essays. We use a hybrid feature identification method, including syntactic structure analysis, rhetorical structure analysis, and topical analysis, to score essay responses from test-takers of the Graduate Management Admissions Test (GMAT) and the Test of Written English (TWE). For each essay question, a stepwise linear regression analysis is run on a training set (sample of human scored essay responses) to extract a weighted set of predictive features for each test question. Score prediction for cross-validation sets is calculated from the set of predictive features. Exact or adjacent agreement between the Electronic Essay Rater (e-rater) score predictions and huma...