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.
TopThe Relevance Of The Gramulator
Identifying indicative features of text types is important to language learners, educators, material designers, and a wide range of researchers from fields such as cognitive science, linguistics, and second language learning. Explicit knowledge of these indicative lexical features helps in several ways. For example, 1) a better understanding of language conventions and practices can be attained, especially when and where two closely related registers meet (e.g., reviewing and summarizing); 2) materials to meet the needs of students can be better prepared, especially where focused practice is called for (e.g., for language learners); 3) a better understanding of register hierarchies to distinguish cases of different practices from cases of advanced practices can be achieved; 4) the characteristics of the moves that constitute the functionality of registers can be identified; and 5) differences between similar texts (e.g., a manipulated text) can be accessed more easily so that potential learning gains can be better established.
Looking at these issues in more detail, we begin with language conventions. The use of indicative lexical features that constitute a genre, register, or variety (or broadly speaking a text type) are typically unconsciously agreed upon conventions (Downs 1998; Hymes 1972). Having knowledge of these conventions is beneficial to those who need or want to diversify their discourse communities because such knowledge provides a greater likelihood of the employment of appropriate comprehension strategies and production formats. At a finer level, awareness of discourse specific language features is facilitative for memory activations, expectations, inferences, depth of comprehension, evaluation of truth and relevance, pragmatic ground-rules, and other psychological mechanisms that depend upon discourse interpretation (Bhatia 1997; Graesser et al. 2002; van Dijk & Kintsch 1983; Zwaan 1993).