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Automated Scoring of Integrative Complexity Using Machine AUTOMATED SCORING OF INTEGRATIVE COMPLEXITY USING MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING by AARDRA KANNAN AMBILI (Under the Direction of Khaled Rasheed) ABSTRACT Conceptual/Integrative complexity is a construct developed in political psychology and clinical psychology to measure an individual’s ability to consider different perspectives on a particular issue and reach a justifiable conclusion after consideration of said perspectives. Integrative complexity (IC) is usually determined from text through manual scoring, which is time- consuming, laborious and expensive. Consequently, there is a demand for automating the scoring, which could significantly reduce the time, expense and cognitive resources spent in the process. Any algorithm that could achieve the above with a reasonable accuracy could assist in the development of intervention systems for reducing the potential for aggression, systems for recruitment processes and even training personnel for improving group complexity in the corporate world. The proposed approach produced classification accuracies ranging from 75% to 83%, which is a first in the literature for automated scoring of integrative complexity. INDEX WORDS: integrative complexity, text classification, semantic similarity, support vector machines AUTOMATED SCORING OF INTEGRATIVE COMPLEXITY USING MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING by AARDRA KANNAN AMBILI B.Tech, Amity University, 2011 A Thesis Submitted to the Graduate Faculty of the University of Georgia in Partial Fulfillment of the Requirements for the Degree MASTER OF SCIENCE ATHENS, GEORGIA 2014 © 2014 Aardra Kannan Ambili All Rights Reserved AUTOMATED SCORING OF INTEGRATIVE COMPLEXITY USING MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING by AARDRA KANNAN AMBILI Major Professor: Khaled Rasheed Committee: Walter D. Potter Adam Goodie Electronic Version Approved: Julie Coffield Interim Dean of the Graduate School The University of Georgia December 2014 AUTOMATED SCORING OF INTEGRATIVE COMPLEXITY USING MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING Aardra Kannan Ambili November, 20, 2014 iv DEDICATION I dedicate this thesis to my mother and my father. Without their constructive criticism, infectious joy and boundless love, I may not have become the person that I am today. v ACKNOWLEDGEMENTS I deeply appreciate and acknowledge the help and support provided to me by Dr. Khaled Rasheed. His sagely advice, sparkly humor and loads of positive energy have been extremely helpful. I would also like to thank all my committee members for their mentorship and timely advice. I would like to take this opportunity to appreciate and acknowledge the time and effort taken by all my teachers who have taught me since kindergarten through high school till graduate school. Finally I would like to thank all my friends for their valuable support and companionship. vi TABLE OF CONTENTS Page ACKNOWLEDGEMENTS ............................................................................................... vi LIST OF TABLES ............................................................................................................. ix CHAPTER 1 INTRODUCTION AND LITERATURE REVIEW .........................................1 1.1 Theoretical Backgrounds .......................................................................2 1.2 Scoring of Integrative Complexity ........................................................6 1.3 Issues faced in designing an Automated Scorer for Integrative Complexity……………………………………………………………….11 1.4 Integrative Complexity as a Predictor for Aggression .........................14 1.5 Integrative Complexity in Politics .......................................................16 1.6 Integrative Complexity and Performance ............................................17 1.7 Concluding Remarks ............................................................................18 2 AUTOMATED SCORING OF INTEGRATIVE COMPLEXITY THROUGH MACHINE LEARNING .................................................................................19 2.1 Abstract ................................................................................................20 2.2 Introduction ..........................................................................................20 2.3 Related Work .......................................................................................23 2.4 Methodology ........................................................................................25 2.5 Evaluation and Results .........................................................................28 vii 2.6 Future Work .........................................................................................32 2.7 Conclusion ...........................................................................................32 3 AUTOMATED SCORING OF INTEGRATIVE COMPLEXITY THROUGH NATURAL LANGUAGE PROCESSING AND MACHINE LEARNING ...34 3.1 Introduction ..........................................................................................35 3.2 Understanding Semantic Paragraph Coherence ...................................36 3.3 Implementation Details for Semantic Paragraph Coherence………..39 3.4 Methodology ........................................................................................44 3.5 Evaluation and Results .........................................................................47 3.6 Conclusion ...........................................................................................50 4 CONCLUSION & FUTURE DIRECTIONS ..................................................51 REFERENCES ..................................................................................................................54 viii LIST OF TABLES Page Table 1: Examples of Integrative Complexity from Senatorial Speeches on Abortion ....10 Table 2.1: Binning of IC scores .........................................................................................26 Table 2.2.: Classification Accuracies .................................................................................30 Table 2.3: Effectiveness measures for Multilayer Perceptron, Multinomial Logistic regression model and the Multi-class classifier ......................................................................30 Table 2.4: Effectiveness measures for SVMs with different kernels .................................31 Table 2.5: Confusion Matrix for the Multinomial Logistic Regression Model .................31 Table 2.6: Confusion Matrix for the Multi-class Classifier ...............................................31 Table 3.1: Effectiveness measures for Bagging, Multi-Class Classifier and Multinomial Logistic Regression with a ridge estimator -II .....................................................................48 Table 3.2: Effectiveness measures for AdaBoostM1- II, Multi-layer perceptron and AdaBoost M1-I. ......................................................................................................................48 Table 3.3: Classification Accuracies ..................................................................................49 Table 3.4: Confusion Matrix for the Multinomial logistic regression with a ridge estimator- II…………………………………………………………………………………..49 Table 3.5: Confusion Matrix for Bagging ..........................................................................50 ix CHAPTER 1 INTRODUCTION AND LITERATURE REVIEW Integrative Complexity is one of “the most-researched operations of the complexity of human thought” (Conway, 2008). Integrative complexity (IC) is used as a measure of the intellectual style used by individuals or groups in processing information, problem solving, and decision making. It is a measure of the structure of one's thoughts, regardless of the contents. The examples below (Statement 1 & Statement 2, both have low IC) are given to show that two text samples of the same topic but with competing views could have the same IC score. Integrative Complexity scorers are therefore required to ignore the content of the text samples and focus on the cognitive strategies involved in formulating the structure of the samples. Statement 1: “The physical poses of Hatha yoga are physically challenging. But the breathing exercises help to balance the energy in your body. The combination of breath work and physical exercises prepare your body for spiritual growth.” Statement 2: “Kundalini yoga focuses on breathing and meditation. For kundalini energy to flow freely, your body must be in harmony. The way to achieve this harmony comes with practicing Hatha yoga. However over-emphasis of Hatha may lead to injury.” Low levels of integrative complexity are characterized by rigid and simplistic perspectives on events. Such narrow simplistic thinking will typecast a single point of 1 view as the correct one and view all other perspectives as illegitimate, flawed, or ridiculous (Suedfeld, Tetlock, & Streufert, 1992). In contrast, thinking characterized as having high levels of IC acknowledge multiple perspectives on an issue and further recognize how these divergent viewpoints connect and contribute to the conclusion. Complex thinkers are more resistant to the influences of singular events and are less susceptible to suggestion and manipulation than simple thinkers. They may also be better able to accommodate stress (Suedfeld & Piedrahita, 1984) and are also better at predicting the behaviors of others and are less prone to projection (Bieri, 1955). 1.1 Theoretical Background The psychological theorization that there does exist stable
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