Abstract
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 extracting features from the eye-movement patterns of annotators and injecting them in popular NLP frameworks. We show that features derived from such forms of cognitive data (referred to as cognition-driven features or cognitive features), when augmented with traditional linguistic features used for well-known supervised machine learning-based NLP systems, improve the performance of such systems. We stick to the tasks of sentiment analysis and sarcasm detection—two well-known problems in text classification.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
