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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."
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