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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(More)
• Abstract Electronic Essay Rater (e-rater) is a prototype automated essay scoring system built at Educational Testing Service (ETS) that uses discourse marking, in addition to syntactic information and topical content vector analyses to automatically assign essay scores. This paper gives a general description ore-rater as a whole, but its emphasis is on(More)
This paper discusses a case study in which lexical semantic techniques were used to implement a prototype scoring system for short-answer, free-responses to test questions. Scoring, as it is discussed in this paper, is a kind of classification problem. Responses are automatically scored by being assigned appropriate classifications. The ultimate goal is to(More)
The proteome of exponentially growing Bacillus subtilis cells was dissected by the implementation of shotgun proteomics and a semigel-based approach for a particular exploration of membrane proteins. The current number of 745 protein identifications that was gained by the use of two-dimensional gel electrophoresis could be increased by 473 additional(More)
This paper describes a prceXype for automatically scoring College Board Advanced Placement (AP) Biology essays. I. The scoring technique used in this study was based on a previous method used to score sentence-length responses (Burstein, et al, 1996). One hundred training essays were used to build an example-based lexicon and concept granunars. The(More)
We present a method for automatically detecting missing hyphens in English text. Our method goes beyond a purely dictionary-based approach and also takes context into account. We evaluate our model on artificially generated data as well as naturally occurring learner text. Our best-performing model achieves high precision and reasonable recall, making it(More)
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