The Gramulator

Abstract

Natural language processing tools, such as Coh-Metrix (see Chapter 11, this volume) and LIWC (see Chapter 12, this volume), have been tremendously successful in offering insight into quantifiable differences between text types. Such quantitative assessments have certainly been highly informative in terms of evaluating theoretical linguistic and psychological categories that distinguish text types (e.g., referential overlap, lexical diversity, positive emotion words, and so forth). Although these identifications are extremely important in revealing ability deficiencies, knowledge gaps, comprehension failures, and underlying psychological phenomena, such assessments can be difficult to interpret because they do not explicitly inform readers and researchers as to which specific linguistic features are driving the text type identification (i.e., the words and word clusters of the text). For example, a tool such as Coh-Metrix informs us that expository texts are more cohesive than narrative texts in terms of sentential referential overlap (McNamara, Louwerse, & Graesser, in press; McCarthy, 2010), but it does not tell us which words (or word clusters) are driving that cohesion. That is, we do not learn which actual words tend to be indicative of the text type differences. These actual words may tend to cluster around certain psychological, cultural, or generic differences, and, as a result, researchers and materials designers who might wish to create or modify text, so as to better meet the needs of readers, are left somewhat in the dark as to which specific language to use. What is needed is a textual analysis tool that offers qualitative output (in addition to quantitative output) that researchers and materials designers might use as a guide to the lexical characteristics of the texts under analysis. The Gramulator is such a tool.