Philip M. McCarthy

Automated Evaluation of Text and Discourse with Coh-Metrix: List of Tables

Coh-Metrix is among the broadest and most sophisticated automated textual assessment tools available today. Automated Evaluation of Text and Discourse with Coh-Metrix describes this computational tool, as well as the wide range of language and discourse measures it provides. Section I of the book focuses on the theoretical perspectives that led to the development of Coh-Metrix, its measures, and empirical work that has been conducted using this approach. Section II shifts to the practical arena, describing how to use Coh-Metrix and how to analyze, interpret, and describe results. Coh-Metrix opens the door to a new paradigm of research that coordinates studies of language, corpus analysis, computational linguistics, education, and cognitive science. This tool empowers anyone with an interest in text to pursue a wide array of previously unanswerable research questions.

Automated Evaluation of Text and Discourse with Coh-Metrix: Acknowledgments

Coh-Metrix is among the broadest and most sophisticated automated textual assessment tools available today. Automated Evaluation of Text and Discourse with Coh-Metrix describes this computational tool, as well as the wide range of language and discourse measures it provides. Section I of the book focuses on the theoretical perspectives that led to the development of Coh-Metrix, its measures, and empirical work that has been conducted using this approach. Section II shifts to the practical arena, describing how to use Coh-Metrix and how to analyze, interpret, and describe results. Coh-Metrix opens the door to a new paradigm of research that coordinates studies of language, corpus analysis, computational linguistics, education, and cognitive science. This tool empowers anyone with an interest in text to pursue a wide array of previously unanswerable research questions.

The components of paraphrase evaluations.

Two sentences are paraphrases if their meanings are equivalent but their words and syntax are different. Paraphrasing can be used to aid comprehension, stimulate prior knowledge, and assist in writing-skills development. As such, paraphrasing is a feature of fields as diverse as discourse psychology, composition, and computer science. Although automated paraphrase assessment is both commonplace and useful, research has centered solely on artificial, edited paraphrases and has used only binary dimensions (i.e., is or is not a paraphrase). In this study, we use an extensive database (N=1,998) of natural paraphrases generated by high school students that have been assessed along 10 dimensions (e.g., semantic completeness, lexical similarity, syntactical similarity). This study investigates the components of paraphrase quality emerging from these dimensions and examines whether computational approaches can simulate those human evaluations. The results suggest that semantic and syntactic evaluations are the primary components of paraphrase quality, and that computationally light systems such as latent semantic analysis (semantics) and minimal edit distances (syntax) present promising approaches to simulating human evaluations of paraphrases.

Automated Evaluation of Text and Discourse with Coh-Metrix: The Importance of Text Cohesion

The need to better understand the important role of cohesion in comprehension was the primary inspiration to develop Coh-Metrix (hence the “Coh” in Coh-Metrix). There has been considerable evidence that cohesion critically determines both how challenging a text is and how well the reader will understand it. Decades of research have demonstrated the importance of cohesion to text comprehension, yet at the turn of this century there were no means available for objectively measuring the cohesion of a text. Studies that had manipulated cohesion (or coherence as it has also often been referred to) had used guidelines to increase or decrease cohesion for any given text version, but there existed no measures of text cohesion itself, particularly measures that could be calculated at large scales (i.e., automatically). This situation presented a clear need to provide researchers and educators with a tool to objectively measure cohesion. As we discussed in Chapter 1, one purpose of Coh-Metrix is to assess the characteristics of the text so that readers’ comprehension can be estimated for that particular text. Notably, however, Coh-Metrix provides estimates of the linguistic, semantic, and discourse characteristics of the text without taking into consideration such fundamental factors as the reader and the task. Any predictions based on Coh-Metrix values for a text should therefore be qualified by the multiple real-world factors that surround the text, including the reader and the task. Readers have varying abilities, knowledge, motivation, and purposes for reading. Tasks vary from reading under duress to reading to enjoy, to learn, and to solve problems. All of these factors potentially interact with the features of the text. A text feature may have one effect in one situation and an entirely different effect in another situation. Such interactions need to be considered carefully when interpreting Coh-Metrix output.

Automated Evaluation of Text and Discourse with Coh-Metrix: Introduction

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Automated Evaluation of Text and Discourse with Coh-Metrix: The Strategy

Coh-Metrix is among the broadest and most sophisticated automated textual assessment tools available today. Automated Evaluation of Text and Discourse with Coh-Metrix describes this computational tool, as well as the wide range of language and discourse measures it provides. Part I of the book focuses on the theoretical perspectives that led to the development of Coh-Metrix, its measures, and empirical work that has been conducted using this approach. Part II shifts to the practical arena, describing how to use Coh-Metrix and how to analyze, interpret, and describe results. Coh-Metrix opens the door to a new paradigm of research that coordinates studies of language, corpus analysis, computational linguistics, education, and cognitive science. This tool empowers anyone with an interest in text to pursue a wide array of previously unanswerable research questions.

Automated Evaluation of Text and Discourse with Coh-Metrix: Coh-Metrix Measures

As we have discussed in the previous chapters, Coh-Metrix was developed to analyze texts on multiple characteristics and levels of language and discourse. Although the original inspiration for the development of Coh-Metrix was to provide automated metrics of text cohesion (hence Coh -Metrix), it became clear very early in the Coh-Metrix project that there was a need in the research community for a more comprehensive tool capable of analyzing texts at multiple language and discourse levels. The Coh-Metrix team has collected and evaluated hundreds of indices since the beginning of the project. The indices scale texts on characteristics related to words, sentences, and connections between sentences. The measures that have been included in Coh-Metrix naturally align with theories of discourse, which assume that comprehension operates at multiple levels (e.g., Graesser & McNamara, 2011; Kintsch, 1998; Snow, 2002). These theoretical frameworks describe representations, structures, and processes at multiple levels of language and discourse. As described in Graesser and McNamara (2011), five levels have been proposed most commonly in these frameworks: (1) words , (2) syntax , (3) the explicit textbase , (4) the situation model , and (5) the discourse genre and rhetorical structure (i.e., the type of discourse and its composition). The theoretical alignment of Coh-Metrix with these levels is described in previous chapters. The number and particular measures provided by Coh-Metrix depend on the version and the type of tool. We have developed public versions of the tool that analyze individual texts and have provided between 40 and 80 theoretically grounded and validated indices. We have also developed internal versions of Coh-Metrix that analyze texts in batches and that include 600–1,000 indices, many of which are redundant and many of which have not been validated (and thus we do not release them to the public). Although the specific Coh-Metrix measures vary somewhat across versions and tools, the banks of measures are quite similar. This chapter describes the indices that are provided in Coh-Metrix 3.0. In this chapter we describe all of those indices in the order they are output in the tool, except those that are associated with readability and text ease, which are described in Chapter 5. The indices that are described in this chapter and Chapter 5 are listed in Appendix A. Comparative norms for each of the indices are provided in Appendix B by grade level for three texts genres (language arts, social studies, and science).