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Coh-Metrix: An Automated Tool for Theoretical and Applied Natural Language Processing

Coh-Metrix: An Automated Tool for Theoretical and Applied Natural Language Processing

Danielle S. McNamara, Arthur C. Graesser
Copyright: © 2012 |Pages: 18
ISBN13: 9781609607418|ISBN10: 1609607414|EISBN13: 9781609607425
DOI: 10.4018/978-1-60960-741-8.ch011
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MLA

McNamara, Danielle S., and Arthur C. Graesser. "Coh-Metrix: An Automated Tool for Theoretical and Applied Natural Language Processing." Applied Natural Language Processing: Identification, Investigation and Resolution, edited by Philip M. McCarthy and Chutima Boonthum-Denecke, IGI Global, 2012, pp. 188-205. https://doi.org/10.4018/978-1-60960-741-8.ch011

APA

McNamara, D. S. & Graesser, A. C. (2012). Coh-Metrix: An Automated Tool for Theoretical and Applied Natural Language Processing. In P. McCarthy & C. Boonthum-Denecke (Eds.), Applied Natural Language Processing: Identification, Investigation and Resolution (pp. 188-205). IGI Global. https://doi.org/10.4018/978-1-60960-741-8.ch011

Chicago

McNamara, Danielle S., and Arthur C. Graesser. "Coh-Metrix: An Automated Tool for Theoretical and Applied Natural Language Processing." In Applied Natural Language Processing: Identification, Investigation and Resolution, edited by Philip M. McCarthy and Chutima Boonthum-Denecke, 188-205. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-60960-741-8.ch011

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Abstract

Coh-Metrix provides indices for the characteristics of texts on multiple levels of analysis, including word characteristics, sentence characteristics, and the discourse relationships between ideas in text. Coh-Metrix was developed to provide a wide range of indices within one tool. This chapter describes Coh-Metrix and studies that have been conducted validating the Coh-Metrix indices. Coh-Metrix can be used to better understand differences between texts and to explore the extent to which linguistic and discourse features successfully distinguish between text types. Coh-Metrix can also be used to develop and improve natural language processing approaches. We also describe the Coh-Metrix Text Easability Component Scores, which provide a picture of text ease (and hence potential challenges). The Text Easability components provided by Coh-Metrix go beyond traditional readability measures by providing metrics of text characteristics on multiple levels of language and discourse.

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