Extraction and Systematic Synthesis of Textual Information by Contextualization and Enrichment : Applications of Automated Reasoning in the Cognitive Sciences
One of the major problems in AI and Deep Learning is Commonsense Reasoning. As easy as it is for humans to perform certain tasks without having to think and waste much time, machines have difficulties in performing those tasks without necessarily been programmed.The Winograd schema is one of the recommended ways for testing the Commonsense reasoning ability of machines. It is difficult for machines to answer this Winograd Schema. In this work, we propose the use of neural network based on Language Models in tackling this problem. Our network takes in only word inputs for the training on large vocabulary size. This model attains an accuracy of
54.58 percent when ran on the Winograd Schema Challenge.
D. L. Poole and A. K. Mackworth, Artificial Intelligence: Foundations of Computational Agents CAMBRIDGE UNIVERSITY PRESS, NY,USA, 2010.
D. Sarkar, R. Bali and T. Sharma, Practical Machine Learning with Python: A Problem-Solvers Guide to Building Real-World Intelligent Systems Springer Science, New York, USA, 2018.
D. Sarkar, Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from your Data Apress, Berkeley,CA, 2016.
E. Davis and G. Marcus, COMMUNICATIONS OF THE ACM 58, 92 (2015).
R. Nakatsu, DIAGRAMMATIC REASONING IN AI John Wiley and Sons, Inc., Hoboken, New Jersey, 2010.
M. P. Bonacina and A. Martelli, Intelligenza Artificiale 3, 14 (2006).
J. McCarthy, Artificial Intelligence, Logic and Formalizing Common Sense (In: Thomason R.H. (eds) Philosophical Logic and Artificial Intelligence. Springer, Dordrecht, 1989).
Y. Jernite, D. Sontag, and A. M. Rus, CoRR abs/1508.06615,(2018).
S. Ye, T. Chua, M. Kan, and L. Qiu, Information Processing and Management 43, 1463 (2007).
T. H. Trinh and Q. V. Le, A Simple Method for Commonsense Reasoning (2018).
Bies, Ann, J. Mott and C. Warner, English News Text Treebank: Penn Treebank Web Download. Philadelphia: Linguistic Data Consortium, 2015.
G. Yoav Neural Network Methods for Natural Language Processing , 2017.
Copyright (c) 2019 IJRDO - Journal of Mathematics (ISSN: 2455-9210)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Author(s) and co-author(s) jointly and severally represent and warrant that the Article is original with the author(s) and does not infringe any copyright or violate any other right of any third parties, and that the Article has not been published elsewhere. Author(s) agree to the terms that the IJRDO Journal will have the full right to remove the published article on any misconduct found in the published article.