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 (Arizona State University, USA) and Arthur C. Graesser (The University of Memphis, USA)
DOI: 10.4018/978-1-60960-741-8.ch011
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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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Readability Vs. Cohesion: Why Coh-Metrix Was Developed

Readability measures are the most common approach to estimating the difficulty of a text and hundreds have been developed over the past century. Readability formulas became popular in the 1950s and by the 1980s over 200 readability algorithms had been developed, with over a 1000 supporting studies (Chall & Dale, 1995; Dubay, 2004). The most well known readability measures include Flesch-Kincaid Grade Level (Klare, 1974-5), Degrees of Reading Power (DRP; Koslin, Zeno, & Koslin, 1987), and Lexile scores (Stenner, 2006). Measures of readability are highly correlated because they are based on the same constructs: the difficulty of the individual words and the complexity of the separate sentences in the text. However, the way in which these constructs are operationalized and the underlying statistical assumptions vary somewhat across readability measures. The Flesch-Kincaid Grade Level metric is based on the length of words (i.e., number of letters or syllables) and length of sentences (i.e., number of words). DRP and Lexile scores relate these characteristics of the texts to readers’ performance on cloze tasks. In a cloze task, the reader reads a text with some words left blank; the reader is asked to fill in the words by generating them or by selecting a word from a set of options (usually the latter). Using this methodology, the appropriateness of a text for a particular reader can be calculated based on the characteristics of the texts and the reader’s performance on cloze tasks. A particular text would be predicated to be at the reader’s level of proficiency if the reader can perform the cloze task at a threshold of performance (75%) for texts with similar characteristics (i.e., with the same word and sentence level difficulties). A text can be defined as too easy if performance is higher than 75% and too difficult to the extent it is lower than 75%.

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