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Introduction

  • Abhijit MishraEmail author
  • Pushpak Bhattacharyya
Chapter
  • 675 Downloads
Part of the Cognitive Intelligence and Robotics book series (CIR)

Abstract

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 the linguistic operations integral to human livelihood and existence, in the form of speech, writing, and multimodal content. The goal of NLP (often otherwise referred to as Computational Linguistics) is to translate linguistic principles and artifacts to and from computer-understandable forms. Why is this important? Well, in the current era of online information explosion, it has become necessary for agencies and individuals to extract and organize critical information from a humongous amount of electronic textual content from Web sites, conversation systems, and other modes of communication. Since manual extraction of such information can be prohibitively expensive, it has become obvious to automatize the process of information gathering from large-scale text. And, NLP provides ways to do that.

References

  1. Agirre, E., & Rigau, G. (1996). Word sense disambiguation using conceptual density. In Proceedings of the 16th Conference on Computational Linguistics (Vol. 1, pp. 16–22). Association for Computational Linguistics.Google Scholar
  2. Anderson, J. R., Bothell, D., & Douglass, S. (2004). Eye movements do not reflect retrieval processes limits of the eye-mind hypothesis. Psychological Science, 15(4), 225–231.Google Scholar
  3. Antonenko, P., Paas, F., Grabner, R., & van Gog, T. (2010). Using electroencephalography to measure cognitive load. Educational Psychology Review, 22(4), 425–438.Google Scholar
  4. Barrett, M., & Søgaard, A. (2015). Using reading behavior to predict grammatical functions. In Proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning (pp. 1–5). Lisbon, Portugal: Association for Computational Linguistics.Google Scholar
  5. Bhattacharyya, P. (2012). Natural language processing: A perspective from computation in presence of ambiguity, resource constraint and multilinguality. CSI Journal of Computing.Google Scholar
  6. Bicknell, K., & Levy, R. (2010). A rational model of eye movement control in reading. In Proceedings of the 48th Annual Meeting of the ACL (pp. 1168–1178). ACL.Google Scholar
  7. Carl, M. (2012). Translog-II: A program for recording user activity data for empirical reading and writing research. In LREC (pp. 4108–4112).Google Scholar
  8. Carl, M. (2013). Dynamic programming for re-mapping noisy fixations in translation tasks. Journal of Eye Movement Research, 6(2), 1–11.Google Scholar
  9. Černỳ, M., & Dobrovolnỳ, M. (2011). Gaze tracking systems for human-computer interface. Perner’s Contact, 6(5), 43–50.Google Scholar
  10. Chennamma, H., & Yuan, X. (2013). A survey on eye-gaze tracking techniques. arXiv:1312.6410.
  11. Demberg, V., & Keller, F. (2008). Data from eye-tracking corpora as evidence for theories of syntactic processing complexity. Cognition, 109(2), 193–210.Google Scholar
  12. Djamasbi, S. (2014). Eye tracking and web experience. AIS Transactions on Human-Computer Interaction, 6(2), 37–54.Google Scholar
  13. Dodge, R., & Cline, T. S. (1901). The angle velocity of eye movements. Psychological Review, 8(2), 145.Google Scholar
  14. Doherty, S., O’Brien, S., & Carl, M. (2010). Eye tracking as an MT evaluation technique. Machine Translation, 24(1), 1–13.Google Scholar
  15. Duchowski, A. (2007). Eye tracking methodology: Theory and practice (Vol. 373). Berlin: Springer Science & Business Media.Google Scholar
  16. Engbert, R., & Krügel, A. (2010). Readers use Bayesian estimation for eye movement control. Psychological Science, 21(3), 366–371.Google Scholar
  17. Engbert, R., Nuthmann, A., Richter, E. M., & Kliegl, R. (2005). Swift: a dynamical model of saccade generation during reading. Psychological Review, 112(4), 777.Google Scholar
  18. Eyegaze, L. (2001). The eyegaze development system a tool for eyetracking applications. LC Technologies Inc.Google Scholar
  19. Hansen, D. W., & Ji, Q. (2010). In the eye of the beholder: A survey of models for eyes and gaze. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(3), 478–500.Google Scholar
  20. Hartridge, H., & Thomson, L. (1948). Methods of investigating eye movements. The British Journal of Ophthalmology, 32(9), 581.Google Scholar
  21. Hornof, A. J., & Halverson, T. (2002). Cleaning up systematic error in eye-tracking data by using required fixation locations. Behavior Research Methods, Instruments, & Computers, 34(4), 592–604.Google Scholar
  22. Huey, E. B. (1908). The psychology and pedagogy of reading: With a review of the history of reading and writing and of methods, texts, and hygiene in reading. New York: The Macmillan Company.Google Scholar
  23. Irwin, D . E. (2004). Fixation location and fixation duration as indices of cognitive processing. The interface of language, vision, and action: Eye movements and the visual world (pp. 105–134). UK: Psychology Press.Google Scholar
  24. Joshi, S., Kanojia, D., & Bhattacharyya, P. (2013). More than meets the eye: Study of human cognition in sense annotation. In NAACL HLT 2013. Atlanta, USA.Google Scholar
  25. Jurafsky, D. (2000). Speech & language processing. India: Pearson Education.Google Scholar
  26. Just, M. A., & Carpenter, P. A. (1980). A theory of reading: from eye fixations to comprehension. Psychological Review, 87(4), 329.Google Scholar
  27. Klerke, S., Goldberg, Y., & Søgaard, A. (2016). Improving sentence compression by learning to predict gaze. arXiv:1604.03357.
  28. Liu, B., & Zhang, L. (2012). A survey of opinion mining and sentiment analysis. Mining Text Data, 415–463.Google Scholar
  29. Lupu, R. G., & Ungureanu, F. (2013). A survey of eye tracking methods and applications. Buletinul Institutului Politehnic Iasi, 71–86.Google Scholar
  30. Martınez-Gómez, P., & Aizawa, A. (2013). Diagnosing causes of reading difficulty using Bayesian networks. In IJCNLP.Google Scholar
  31. Mishra, A., Carl, M., & Bhattacharyya, P. (2012). A heuristic-based approach for systematic error correction of gaze data for reading. In Proceedings of the First Workshop on Eyetracking and Natural Language Processing. Mumbai, India.Google Scholar
  32. Navigli, R. (2009). Word sense disambiguation: A survey. ACM Computing Surveys (CSUR), 41(2), 10.Google Scholar
  33. Orman, Z., Battal, A., & Kemer, E. (2011). A study on face, eye detection and gaze estimation. IJCSES, 2(3), 29–46.Google Scholar
  34. Paas, F., Tuovinen, J. E., Tabbers, H., & Van Gerven, P. W. (2003). Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist, 38(1), 63–71.Google Scholar
  35. Parasuraman, R., & Rizzo, M. (2006). Neuroergonomics: The brain at work. Oxford: Oxford University Press.Google Scholar
  36. Raganato, A., Bovi, C. D., & Navigli, R. (2017). Neural sequence learning models for word sense disambiguation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (pp. 1167–1178).Google Scholar
  37. Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychological bulletin, 124(3), 372.Google Scholar
  38. Rayner, K., & Duffy, S. A. (1986). Lexical complexity and fixation times in reading: Effects of word frequency, verb complexity, and lexical ambiguity. Memory & Cognition, 14(3), 191–201.Google Scholar
  39. Reichle, E. D., Pollatsek, A., & Rayner, K. (2006). E-Z reader: A cognitive-control, serial-attention model of eye-movement behavior during reading. Cognitive Systems Research, 7(1), 4–22.Google Scholar
  40. Reichle, E. D., Rayner, K., & Pollatsek, A. (2003). The EZ reader model of eye-movement control in reading: Comparisons to other models. Behavioral and Brain Sciences, 26(04), 445–476.Google Scholar
  41. Solso, R. L. (1996). Cognition and the visual arts. Cambridge: MIT Press.Google Scholar
  42. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive science, 12(2), 257–285.Google Scholar
  43. Von der Malsburg, T., & Vasishth, S. (2011). What is the scanpath signature of syntactic reanalysis? Journal of Memory and Language, 65(2), 109–127.Google Scholar
  44. Wood, E., & Bulling, A. (2014). Eyetab: Model-based gaze estimation on unmodified tablet computers. In Proceedings of the Symposium on Eye Tracking Research and Applications (pp. 207–210). ACM.Google Scholar
  45. Yamamoto, M., Nakagawa, H., Egawa, K., & Nagamatsu, T. (2013). Development of a mobile tablet PC with gaze-tracking function. Human interface and the management of information. Information and interaction for health, safety, mobility and complex environments (pp. 421–429). Berlin: Springer.Google Scholar
  46. Yamaya, A., Topić, G., Martínez-Gómez, P., & Aizawa, A. (2015). Dynamic-programming–based method for fixation-to-word mapping. In Intelligent Decision Technologies (pp. 649–659). Berlin: Springer.Google Scholar
  47. Yarbus, A. (1967). Eye movements and vision. New York: Plenum.Google Scholar
  48. Zhang, Y., & Hornof, A. J. (2011). Mode-of-disparities error correction of eye-tracking data. Behavior Research Methods, 43(3), 834–842.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.India Research LabIBM ResearchBangaloreIndia
  2. 2.Indian Institute of Technology PatnaPatnaIndia

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