scholarly journals Quantification of textual comprehension difficulty with an information theory-based algorithm

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Louise Bogéa Ribeiro ◽  
Anderson Raiol Rodrigues ◽  
Kauê Machado Costa ◽  
Manoel da Silva Filho
Author(s):  
Maryam Sadat Mirzaei ◽  
Kourosh Meshgi

This paper investigates the effect of sentence complexity, specifically lexical and syntactic surprisal, on L2 listening difficulty. Psycholinguistic studies revealed that surprisal cases correlate with textual comprehension difficulty. Based on surprisal theory, these cases are less probable or expected, considering the precedent context, thus require more complex processing to comprehend. Little is known about the influence of the surprisal factor on L2 listening comprehension. We aim to examine this effect and propose to include these cases in captioning to assist L2 listeners. Since conventional captions include the whole transcript, we use Partial and Synchronized Caption (PSC) with limited textual clues, which allows for highlighting surprisal cases to reduce ambiguity. In our experiment, intermediate learners of English (undergraduates) were asked to transcribe and paraphrase videos containing surprisal cases. Results revealed that learners faced difficulty when encountering surprisal, which was partially addressed with the help of PSC, yet more assistance was required.


Author(s):  
Charles A. Doan ◽  
Ronaldo Vigo

Abstract. Several empirical investigations have explored whether observers prefer to sort sets of multidimensional stimuli into groups by employing one-dimensional or family-resemblance strategies. Although one-dimensional sorting strategies have been the prevalent finding for these unsupervised classification paradigms, several researchers have provided evidence that the choice of strategy may depend on the particular demands of the task. To account for this disparity, we propose that observers extract relational patterns from stimulus sets that facilitate the development of optimal classification strategies for relegating category membership. We conducted a novel constrained categorization experiment to empirically test this hypothesis by instructing participants to either add or remove objects from presented categorical stimuli. We employed generalized representational information theory (GRIT; Vigo, 2011b , 2013a , 2014 ) and its associated formal models to predict and explain how human beings chose to modify these categorical stimuli. Additionally, we compared model performance to predictions made by a leading prototypicality measure in the literature.


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