A Feature-based Classification Technique for Answering Multi-choice World History Questions: FRDC_QA at NTCIR-11 QA-Lab Task

Our FRDC_QA team participated in the QA-Lab English subtask of the NTCIR-11. In this paper, we describe our system for solving real-world university entrance exam questions, which are related to world history. Wikipedia is used as the main external resource for our system. Since problems with choosing right/wrong sentence from multiple sentence choices account for about two-thirds of the total, we individually design a classification based model for solving this type of questions. For other types of questions, we also design some simple methods.

[1]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[2]  Geoffrey I. Webb,et al.  MultiBoosting: A Technique for Combining Boosting and Wagging , 2000, Machine Learning.

[3]  Andrew W. Moore,et al.  Locally Weighted Learning , 1997, Artificial Intelligence Review.

[4]  M. Haggag,et al.  The Question Answering Systems : A Survey . , 2016 .

[5]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[6]  Bettina Sorger,et al.  Another kind of 'BOLD Response': answering multiple-choice questions via online decoded single-trial brain signals. , 2009, Progress in brain research.

[7]  Veronika Kopp,et al.  Answering multiple‐choice questions in high‐stakes medical examinations , 2005, Medical education.

[8]  岩坂正和 音楽イメージにおける身体性表現にかかわる脳血量ダイナミクス. The 23rd Annual Conference of the Japanese Society for Artificial Intelligence , 2009 .

[9]  Pat Langley,et al.  Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.

[10]  Andreas Rauber,et al.  Web-Based Multiple Choice Question Answering for English and Arabic Questions , 2006, ECIR.

[11]  Yoshinori Watanabe,et al.  The National Center Test for University Admissions , 2013 .

[12]  Proceedings of the 11th NTCIR Conference on Evaluation of Information Access Technologies, NTCIR-11, National Center of Sciences, Tokyo, Japan, December 9-12, 2014 , 2014, NTCIR.

[13]  Dunja Mladenic,et al.  Extracting Named Entities and Relating Them over Time Based on Wikipedia , 2007, Informatica.

[14]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[15]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[16]  Eibe Frank,et al.  Logistic Model Trees , 2003, Machine Learning.

[17]  J. Friedman Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .

[18]  Günther Eibl,et al.  How to Make AdaBoost.M1 Work for Weak Base Classifiers by Changing Only One Line of the Code , 2002, ECML.

[19]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[20]  Hongyun Bao,et al.  Detecting dynamic association among twitter topics , 2012, WWW.