Using Psycholinguistic Features for the Classification of Comprehenders from Summary Speech Transcripts
Santosh Kumar Barnwal, Uma Shanker Tiwary · Lecture notes in computer science · 2017
In education, some students lack language comprehension, language production and language acquisition skills. In this paper we extracted several psycholinguistics features broadly grouped into lexical and morphological complexity, syntactic complexity, production units, syntactic pattern density, referential cohesion, connectives, amounts of coordination, amounts of subordination, LSA, word information, and readability from students’ summary speech transcripts. Using these Coh-Metrix features, comprehenders are classified into two groups: poor comprehender and proficient comprehender. It is concluded that a computational model can be implemented using a reduced set of features and the results can be used to help poor reading comprehenders for improving their cognitive reading skills.