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 examining the performance of a simple unigram baseline that achieves surprisingly good results for some of the datasets. We then show how the recall of the system can be improved over this strong baseline.

The Common Core Standards call for students to be exposed to a much greater level of text complexity than has been the norm in schools for the past forty years. Textbook publishers, teachers, and assessment developers are being asked to refocus materials and methods to ensure that students are challenged to read texts at steadily increasing complexity levels as they progress through school so that all students remain on track to achieve college and career readiness by the end of 12th grade. Although automated text analysis tools have been proposed as one method for helping educators achieve this goal, research suggests that existing tools are subject to three limitations: inadequate construct coverage; overly narrow criterion variables; and inappropriate treatment of genre effects. Modeling approaches developed to address these limitations are described. Recommended approaches are incorporated into a new text analysis system called SourceRater. Validity analyses implemented on an independent sample of texts suggest that, compared to existing approaches, SourceRater's estimates of text complexity are more reflective of the complexity classifications given in the new Standards. Implications for the development of learning progressions designed to help educators organize curriculum, instruction and assessment in reading are discussed.

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 these classes for classifying all verbs in a running text as metaphor or non metaphor.

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 that while the first manipulation was very effective, the second was only slightly so. Possible reasons for these observations are discussed.

We present a novel situational task that integrates collaborative problem solving behavior with testing in a science domain. Participants engage in discourse, which is used to evaluate their collaborative skills. We present initial experiments for automatic classification of such discourse, using a novel classification schema. Considerable accuracy is achieved with just lexical features. A speech-act classifier, trained on out-of-domain data, can also be helpful.