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Current approaches to supervised learning of metaphor tend to use sophisticated features and restrict their attention to constructions and contexts where these features apply. In this paper, we describe the development of a supervised learning system to classify all content words in a running text as either being used metaphori-cally or not. We start by(More)
We present a supervised machine learning system for word-level classification of all content words in a running text as being metaphori-cal or non-metaphorical. The system provides a substantial improvement upon a previously published baseline, using re-weighting of the training examples and using features derived from a concreteness database. We observe(More)
In this paper we present a new spell-checking system that utilizes contextual information for automatic correction of non-word misspel-lings. The system is evaluated with a large corpus of essays written by native and non-native speakers of English to the writing prompts of high-stakes standardized tests (TOEFL ® and GRE ®). We also present comparative(More)
As part of its nonprofit mission, ETS conducts and disseminates the results of research to advance quality and equity in education and assessment for the benefit of ETS's constituents and the field. To obtain a PDF or a print copy of a report, please visit: Abstract The Common Core Standards call for students to be exposed to a much greater level of text(More)
We describe a new representation of the content vocabulary of a text we call word association profile that captures the proportions of highly associated, mildly associated , unassociated, and dis-associated pairs of words that co-exist in the given text. We illustrate the shape of the dis-tirbution and observe variation with genre and target audience. We(More)
Developments in the educational landscape have spurred greater interest in the problem of automatically scoring short answer questions. A recent shared task on this topic revealed a fundamental divide in the mod-eling approaches that have been applied to this problem, with the best-performing systems split between those that employ a knowledge engineering(More)