Michael Flor

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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 metaphorically or not. We start by(More)
This paper presents an investigation on using four types of contextual information for improving the accuracy of automatic correction of single-token non-word misspellings. The task is framed as contextually-informed re-ranking of correction candidates. Immediate local context is captured by word n-grams statistics from a Web-scale language model. The(More)
We present a supervised machine learning system for word-level classification of all content words in a running text as being metaphorical 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 misspellings. The system is evaluated with a large corpus of essays written by native and nonnative speakers of English to the writing prompts of high-stakes standardized tests (TOEFL and GRE). We also present comparative(More)
We investigate the effectiveness of semantic generalizations/classifications for capturing the regularities of the behavior of verbs in terms of their metaphoricity. Starting from orthographic word unigrams, we experiment with various ways of defining semantic classes for verbs (grammatical, resource-based, distributional) and measure the effectiveness of(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 distirbution and observe variation with genre and target audience. We(More)