Natural language processing and discourse complexity studies

Abstract

The study presents an overview of discursive complexology, an integral paradigm of linguistics, cognitive studies and computer linguistics aimed at defining discourse complexity. The article comprises three main parts, which successively outline views on the category of linguistic complexity, history of discursive complexology and modern methods of text complexity assessment. Distinguishing the concepts of linguistic complexity, text and discourse complexity, we recognize an absolute nature of text complexity assessment and relative nature of discourse complexity, determined by linguistic and cognitive abilities of a recipient. Founded in the 19th century, text complexity theory is still focused on defining and validating complexity predictors and criteria for text perception difficulty. We briefly characterize the five previous stages of discursive complexology: formative, classical, period of closed tests, constructive-cognitive and period of natural language processing. We also present the theoretical foundations of Coh-Metrix, an automatic analyzer, based on a five-level cognitive model of perception. Computing not only lexical and syntactic parameters, but also text level parameters, situational models and rhetorical structures, Coh-Metrix provides a high level of accuracy of discourse complexity assessment. We also show the benefits of natural language processing models and a wide range of application areas of text profilers and digital platforms such as LEXILE and ReaderBench. We view parametrization and development of complexity matrix of texts of various genres as the nearest prospect for the development of discursive complexology which may enable a higher accuracy of inter- and intra-linguistic contrastive studies, as well as automating selection and modification of texts for various pragmatic purposes.

Keywords

• text complexity
• discourse
• cognitive model
• automatic analyzer
• natural language processing