Advertisement

Cognitively Inspired Natural Language Processing

An Investigation Based on Eye-tracking

  • Abhijit Mishra
  • Pushpak Bhattacharyya
Book

Part of the Cognitive Intelligence and Robotics book series (CIR)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Abhijit Mishra, Pushpak Bhattacharyya
    Pages 1-21
  3. Abhijit Mishra, Pushpak Bhattacharyya
    Pages 23-46
  4. Assessing Cognitive Effort in Annotation

    1. Front Matter
      Pages 47-47
    2. Abhijit Mishra, Pushpak Bhattacharyya
      Pages 49-76
    3. Abhijit Mishra, Pushpak Bhattacharyya
      Pages 77-98
    4. Abhijit Mishra, Pushpak Bhattacharyya
      Pages 99-115
  5. Extracting Cognitive Features for Text Classification

    1. Front Matter
      Pages 117-117
    2. Abhijit Mishra, Pushpak Bhattacharyya
      Pages 119-152
    3. Abhijit Mishra, Pushpak Bhattacharyya
      Pages 153-169
    4. Abhijit Mishra, Pushpak Bhattacharyya
      Pages 171-174

About this book

Introduction

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.”

Keywords

Eye-tracking and NLP Cognitive NLP Cognitive Computational Linguistics Eye-movement Data for NLP Cognitive Features for Classification

Authors and affiliations

  • Abhijit Mishra
    • 1
  • Pushpak Bhattacharyya
    • 2
  1. 1.India Research LabIBM ResearchBangaloreIndia
  2. 2.Indian Institute of Technology PatnaPatnaIndia

Bibliographic information