Cognitively Inspired Natural Language Processing
An Investigation Based on Eye-tracking
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10 Result(s)
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Chapter
Multilingual Projections
Languages of the world, though different, share structures and vocabulary. Today’s NLP depends crucially on annotation which, however, is costly, needing expertise, money and time. Most languages in the world ...
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Book
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Chapter
Epilogue
The book presented an approach to leveraging cognitive features for NLP by harnessing eye-movement information from human readers and annotators. Eye-tracking technology is primarily used to record and analyze sh...
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Chapter
Applications of Eye Tracking in Language Processing and Other Areas
This chapter presents an overview on applications of eye-tracking technology in natural language processing (NLP) and other areas. While traditional NLP techniques mostly rely on the textual properties, recent...
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Chapter
Scanpath Complexity: Modeling Reading/Annotation Effort Using Gaze Information
In the previous chapter, we discussed how cognitive information derived from the eye-movement patterns of the annotators can be used to model annotation complexity for translation and sentiment annotation. We ...
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Chapter
Harnessing Cognitive Features for Sentiment Analysis and Sarcasm Detection
This chapter begins the second part of the thesis in which we demonstrate how cognitive information can be harnessed for NLP, specifically for text classification. We discuss several possibilities of extractin...
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Chapter
Introduction
Natural language processing (NLP) is concerned with interactions between computers and human through the medium of languages. NLP is founded on the science of linguistics, whose aim it is to gain insight into ...
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Chapter
Estimating Annotation Complexities of Text Using Gaze and Textual Information
The basic requirement of supervised data-driven methods for various NLP tasks like part-of-speech tagging, dependency parsing, machine translation is large-scale annotated data. Since statistical methods have tak...
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Chapter
Predicting Readers’ Sarcasm Understandability by Modeling Gaze Behavior
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 a...
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Chapter
Automatic Extraction of Cognitive Features from Gaze Data
Cognitive NLP systems—i.e., NLP systems that make use of behavioral data—augment traditional text-based features with cognitive features extracted from eye-movement patterns, EEG signals, brain imaging, etc. S...
