Intro; Preface; Acknowledgements; Contents; About the Authors; 1 Introduction; 1.1 Cognitive Data: A Valuable By-product of Annotation; 1.2 Human Eye-Movement and Eye-Tracking Technology; 1.2.1 The Visual System: How Do We See?; 1.2.2 Eye-Tracking Technology; 1.2.3 History of Development of Eye-Trackers; 1.2.4 Eye-Tracking Systems: Invasive and Non-invasive; 1.2.5 Tools for Gaze Data Recording and Analysis; 1.2.6 Eye Movement in Reading and Language Processing; 1.3 Theme of the Monograph; 1.3.1 Research Objective 1: Assessing Cognitive Effort in Text Annotation

1.3.2 Research Objective 2: Extracting Cognitive Features for Text Classification1.4 Roadmap of the Book; References; 2 Applications of Eye Tracking in Language Processing and Other Areas; 2.1 Eye Movement and Reading: A Psycholinguistic Perspective; 2.1.1 The Eye-Mind Hypothesis: Just and Carpenters' Theory of Reading; 2.1.2 Basic Characteristics of Eye Movement in Reading; 2.1.3 Effects of Lexical and Syntactic Complexities on Eye Movement; 2.1.4 Models for Eye-Movement Control During Reading; 2.1.5 Comparing Eye-Movement Patterns: Measures for Scanpath Similarity

2.2 Eye-Movement Behavior and Text Annotation2.2.1 Study of Text Translation Annotation; 2.2.2 Study of Word Sense Annotation; 2.2.3 Study of Sentiment Annotation; 2.2.4 Cognitive Cost Model for Annotation-A Case Study of Named Entity Marking; 2.3 Eye-Movement Data for Development and Evaluation of NLP Systems; 2.3.1 Part-of-Speech Tagging; 2.3.2 Sentence Compression; 2.3.3 Machine Translation Evaluation; 2.4 Eye Tracking: Application Areas Other than Reading and Language Processing; 2.4.1 Neuroscience; 2.4.2 Industrial Engineering and Human Factors

2.4.3 Human-Computer Interaction and User Experience2.4.4 Marketing/Advertisement; References; Part I Assessing Cognitive Effort in Annotation; 3 Estimating Annotation Complexities of Text Using Gaze and Textual Information; 3.1 Estimating Text Translation Complexity; 3.1.1 Translation Complexity Index-Motivation, Utility, and Background; 3.1.2 Prediction Framework for Translation Complexity; 3.1.3 Using Eye Tracking for TCI Annotation; 3.1.4 Computing TCI Using Eye-Tracking Database; 3.1.5 Relating TCI to Linguistic Features; 3.1.6 Lexical Features; 3.1.7 Syntactic Features

3.1.8 Semantic Features3.1.9 Translation Feature; 3.1.10 Experiment and Results; 3.1.11 Discussion: Correlation Between Translation Complexity and Machine Translation Accuracy; 3.2 Measuring Sentiment Annotation Complexity; 3.2.1 Sentiment Annotation Complexity: Motivation, Utility and Background; 3.2.2 Understanding Sentiment Annotation Complexity; 3.2.3 Creation of Dataset Annotated with SAC; 3.2.4 Eye-Tracking Experimental Setup; 3.2.5 Calculating SAC from Eye-Movement Data; 3.2.6 Linguistic Features for Predicting Sentiment Annotation Complexity; 3.2.7 Predictive Framework for SAC