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Text content variables as a function of comprehension: Propositional discourse analysis

Journal: Russian Journal of Linguistics (Vol.27, No. 4)

Publication Date:

Authors : ; ; ;

Page : 938-956

Keywords : propositions; text complexity; reading comprehension; cognitive model; automatic text analyzer; natural language processing;

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Abstract

Text complexity impact on immediate recalls and range of metadiscourse markers remains a research niche due to the lack of multidisciplinary data necessary to shed light on the issue. The current study aims to identify effects of text complexity and Russian-English discourse differences on immediate text-based recalls relating to the amount and type of the information reproduced. For the research purposes we engaged 94 native Russian speakers as respondents in a text-retelling task to explore the amount of propositions recalled from an opinion article and the range of discourse markers employed. The reading text and text-based recalls were contrasted on informative and linguistic levels. The informative complexity of the reading text was evaluated on the basis of propositional analysis, and the linguistic complexity was carried out on the basis of descriptive parameters (word and sentence length, proportion of long words), readability index, word complexity and range of metadiscourse markers. The study revealed that the complexity level of the reading text is a strong predictor of propositional recall. The comparative analysis indicated a slight decrease in metrics of descriptive parameters. We also revealed that high ability readers make a choice in favor of superordinate propositions recalling about 60% of them and losing over 70% of the subordinate propositions. They also tend to shift the metadiscourse patterns of the original text from interactive to more logical ones by loosing hedges, emphatics and evidentials. The study furthers our understanding of cross-linguistic differences in the use of metadiscourse, its results will find application in discourse complexology and natural language processing.

Last modified: 2024-04-01 17:46:35