Abstract
In the previous two chapters, we demonstrated how cognitive effort in text annotation can be assessed by utilizing cognitive information obtained from readers’/annotators’ eye-gaze patterns. While our models are, to some extent, effective in modeling various forms of complexities at the textual side, we observed that cognitive information can also be useful to model the ability of a reader to understand/comprehend the given reading material. This observation was quite clear in our sentiment annotation experiment (discussed in Chap. 3), where the eye-movement patterns of some of our annotators appeared to be subtle when the text had linguistic nuances like sarcasm, which the annotators failed to recognize. This motivated us to work on a highly specific yet important problem of sarcasm understandability prediction—a starting step toward an even more important problem of modeling text comprehensibility.
Declaration: Consent of the subjects participating in the eye-tracking experiments for collecting data used for the work reported in this chapter has been obtained.
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Notes
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Source: The Free Dictionary.
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Two-tailed assuming unequal variance.
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The system performs badly, as expected, in a non-MI setting. The F-scores for SVM and logistic regression classifiers are as low as 30%. Hence, they are not reported here.
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Mishra, A., Bhattacharyya, P. (2018). Predicting Readers’ Sarcasm Understandability by Modeling Gaze Behavior. In: Cognitively Inspired Natural Language Processing. Cognitive Intelligence and Robotics. Springer, Singapore. https://doi.org/10.1007/978-981-13-1516-9_5
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