- ホーム
- > 洋書
- > 英文書
- > Computer / General
Full Description
This book shows ways of augmenting the capabilities of Natural Language Processing (NLP) systems by means of cognitive-mode language processing. The authors employ eye-tracking technology to record and analyze shallow cognitive information in the form of gaze patterns of readers/annotators who perform language processing tasks. The insights gained from such measures are subsequently translated into systems that help us (1) assess the actual cognitive load in text annotation, with resulting increase in human text-annotation efficiency, and (2) extract cognitive features that, when added to traditional features, can improve the accuracy of text classifiers. In sum, the authors' work successfully demonstrates that cognitive information gleaned from human eye-movement data can benefit modern NLP.
Currently available Natural Language Processing (NLP) systems are weak AI systems: they seek to capture the functionality of human language processing, without worrying about how thisprocessing is realized in human beings' hardware. In other words, these systems are oblivious to the actual cognitive processes involved in human language processing. This ignorance, however, is NOT bliss! The accuracy figures of all non-toy NLP systems saturate beyond a certain point, making it abundantly clear that "something different should be done."
Contents
Chapter 1. Introduction.- Chapter 2. Eye-tracking: Theory, Methods, and Applications in Language Processing and Other Areas.- Chapter 3. Estimating Annotation Complexities of Text Using Gaze and Textual Information - Case studies of Translation and Sentiment Annotation.- Chapter 4. Scanpath Complexity: Combining Gaze Attributes for Modeling Effort in Text Reading/Annotation.- Chapter 5. Predicting Readers' Sarcasm Understandability by Modeling Gaze Behavior.- Chapter 6. Harnessing Cognitive Features for Sentiment Analysis and Sarcasm Detection.- Chapter 7. Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network.- Chapter 8. Conclusion and Future Directions.
