Cognitively Inspired Natural Language Processing
An Investigation Based on Eye-tracking
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Beschreibung
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 this processing 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.”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.”
The first of its kind book, shedding new light on how modern Natural Language Processing (NLP) systems can leverage human cognitive data Includes the authors’ research contributions that are “relevant,” “timely” and ”influential” in the context of modern NLP Shows that cognitively inspired NLP (Cognitive NLP) is currently drawing attention from Computational Linguistic community with a good amount of research work on Cognitive NLP getting published in top-tier NLP conferences and journals A highly useful guide for all researchers interested in pursuing this research direction Explains research methodologies and makes the authors’ experiments easily replicable; some of the authors’ systems are also hosted online and are easy to use Demonstrates that eye tracking devices are becoming widespread and easily available, even on mobile phones, and shows how to seize the opportunity of an enabling technology to design better NLP systems
Autor*in
Abhijit Mishra
Themen in »Cognitively Inspired Natural Language Processing«
Eye-tracking and NLP Cognitive NLP Cognitive Computational Linguistics Eye-movement Data for NLP Cognitive Features for Classification