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        <namePart type="given">Rajkumar</namePart>
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    <abstract>Eye-tracking reading times have been attested to reflect cognitive processes underlying sentence comprehension. However, the use of reading times in NLP applications is an underexplored area of research. In this initial work we build an automatic system to assess sentence complexity using automatically predicted eye-tracking reading time measures and demonstrate the efficacy of these reading times for a well known NLP task, namely, readability assessment. We use a machine learning model and a set of features known to be significant predictors of reading times in order to learn per-word reading times from a corpus of English text having reading times of human readers. Subsequently, we use the model to predict reading times for novel text in the context of the aforementioned task. A model based only on reading times gave competitive results compared to the systems that use extensive syntactic features to compute linguistic complexity. Our work, to the best of our knowledge, is the first study to show that automatically predicted reading times can successfully model the difficulty of a text and can be deployed in practical text processing applications.</abstract>
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%0 Conference Proceedings
%T Quantifying sentence complexity based on eye-tracking measures
%A Singh, Abhinav Deep
%A Mehta, Poojan
%A Husain, Samar
%A Rajakrishnan, Rajkumar
%Y Brunato, Dominique
%Y Dell’Orletta, Felice
%Y Venturi, Giulia
%Y François, Thomas
%Y Blache, Philippe
%S Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F singh-etal-2016-quantifying
%X Eye-tracking reading times have been attested to reflect cognitive processes underlying sentence comprehension. However, the use of reading times in NLP applications is an underexplored area of research. In this initial work we build an automatic system to assess sentence complexity using automatically predicted eye-tracking reading time measures and demonstrate the efficacy of these reading times for a well known NLP task, namely, readability assessment. We use a machine learning model and a set of features known to be significant predictors of reading times in order to learn per-word reading times from a corpus of English text having reading times of human readers. Subsequently, we use the model to predict reading times for novel text in the context of the aforementioned task. A model based only on reading times gave competitive results compared to the systems that use extensive syntactic features to compute linguistic complexity. Our work, to the best of our knowledge, is the first study to show that automatically predicted reading times can successfully model the difficulty of a text and can be deployed in practical text processing applications.
%U https://aclanthology.org/W16-4123
%P 202-212

Markdown (Informal)

[Quantifying sentence complexity based on eye-tracking measures](https://aclanthology.org/W16-4123) (Singh et al., CL4LC 2016)

• Quantifying sentence complexity based on eye-tracking measures (Singh et al., CL4LC 2016)

ACL

• Abhinav Deep Singh, Poojan Mehta, Samar Husain, and Rajkumar Rajakrishnan. 2016. Quantifying sentence complexity based on eye-tracking measures. In Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC), pages 202–212, Osaka, Japan. The COLING 2016 Organizing Committee.