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Exploiting Wikipedia Semantics for Computing Word Associations

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Exploiting Wikipedia Semantics for Computing Word Associations Exploiting Wikipedia Semantics for Computing Word Associations by Shahida Jabeen A thesis submitted to the Victoria University of Wellington in fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science. Victoria University of Wellington 2014 Abstract Semantic association computation is the process of automatically quan- tifying the strength of a semantic connection between two textual units based on various lexical and semantic relations such as hyponymy (car and vehicle) and functional associations (bank and manager). Humans have can infer implicit relationships between two textual units based on their knowledge about the world and their ability to reason about that knowledge. Automatically imitating this behavior is limited by restricted knowledge and poor ability to infer hidden relations. Various factors affect the performance of automated approaches to com- puting semantic association strength. One critical factor is the selection of a suitable knowledge source for extracting knowledge about the im- plicit semantic relations. In the past few years, semantic association com- putation approaches have started to exploit web-originated resources as substitutes for conventional lexical semantic resources such as thesauri, machine readable dictionaries and lexical databases. These conventional knowledge sources suffer from limitations such as coverage issues, high construction and maintenance costs and limited availability. To overcome these issues one solution is to use the wisdom of crowds in the form of collaboratively constructed knowledge sources. An excellent example of such knowledge sources is Wikipedia which stores detailed information not only about the concepts themselves but also about various aspects of the relations among concepts. The overall goal of this thesis is to demonstrate that using Wikipedia for computing word association strength yields better estimates of hu- mans’ associations than the approaches based on other structured and un- structured knowledge sources. There are two key challenges to achieve this goal: first, to exploit various semantic association models based on different aspects of Wikipedia in developing new measures of semantic associations; and second, to evaluate these measures compared to human performance in a range of tasks. The focus of the thesis is on exploring two aspects of Wikipedia: as a formal knowledge source, and as an informal text corpus. The first contribution of the work included in the thesis is that it ef- fectively exploited the knowledge source aspect of Wikipedia by devel- oping new measures of semantic associations based on Wikipedia hyper- link structure, informative-content of articles and combinations of both elements. It was found that Wikipedia can be effectively used for comput- ing noun-noun similarity. It was also found that a model based on hybrid combinations of Wikipedia structure and informative-content based fea- tures performs better than those based on individual features. It was also found that the structure based measures outperformed the informative- content based measures on both semantic similarity and semantic related- ness computation tasks. The second contribution of the research work in the thesis is that it ef- fectively exploited the corpus aspect of Wikipedia by developing a new measure of semantic association based on asymmetric word associations. The thesis introduced the concept of asymmetric associations based mea- sure using the idea of directional context inspired by the free word asso- ciation task. The underlying assumption was that the association strength can change with the changing context. It was found that the asymmetric- association based measure performed better than the symmetric measures on semantic association computation, relatedness based word choice and causality detection tasks. However, asymmetric-associations based mea- sures have no advantage for synonymy-based word choice tasks. It was also found that Wikipedia is not a good knowledge source for capturing verb-relations due to its focus on encyclopedic concepts specially nouns. It is hoped that future research will build on the experiments and dis- cussions presented in this thesis to explore new avenues using Wikipedia for finding deeper and semantically more meaningful associations in a wide range of application areas based on humans’ estimates of word asso- ciations. iv Acknowledgment “The difference between a successful person and others is not a lack of strength, nor a lack of knowledge, but rather a lack of will”. Three years ago, this will motivated me to leave my family, my job and my country to pursue my career in research. Three precious years of my life in New Zealand have been full of experiences that taught me the value of hard work in life. This thesis is an outcome of the support, help and sacrifice of many priceless people who have had a great influence on my life and studies over the last three years. I greatly admire my supervisors, Dr. Sharon Gao and Dr. Peter Andreae, for believing in me. I am indebted to them for their outstanding support and mentoring. Our weekly meetings have undoubtedly been key in keeping me focused and giving me the courage to achieve my goals. I would also like to express my gratitude to Prof. Mengjie Zhang, for being very supportive and encouraging. His success celebrations and Christ- mas BBQ’s have always been wonderfully exciting. I wish to thank ECRG group for creating an insightful and friendly research environment. I am also grateful to Peter D. Turney (National Research Council of Canada) for providing me datasets for the word choice task. I also want to thank Thomas K. Landauer (LSA and NLP Lab, University of Colorado) for pro- viding the TOEFL dataset. Last but not the least, I want to thank my family for their continuous support, help and prayers and my beloved husband for his unconditional love. It would not have been possible for me to complete my journey without his support and encouragement. v vi List of Tables 2.1 Correlation of the association strength of three terms of se- mantic associations. 20 2.2 Statistics of Benchmark datasets used for evaluation of se- mantic association measures. 28 2.3 Examples of approaches using knowledge-sources and text corpora in various applications. 33 2.4 Some well-known text corpora used by corpus-based ap- proaches. 37 3.1 Performance comparison of the semantic measures on all- terms (All) versions of M&C, R&G and WS-353 datasets. 87 3.2 Performance comparison of the semantic measures on Non- Missing (NM) versions of M&C, R&G and WS-353 datasets. 88 3.3 Performance comparison of semantic measures with WLM measure on all-terms (All) versions of M&C, R&G and WS- 353 datasets. 88 3.4 Performance comparison of the semantic measures on M&C, R&G and WS-353 datasets using manual and automatic dis- ambiguations. 90 3.5 Performance comparison of the semantic measures on all- terms versions of similarity (WS-353-(S)) and relatedness (WS- 353-(R)) based subsets of the WS-353 dataset. 93 vii viii LIST OF TABLES 3.6 Performance comparison of the semantic measures on Non- Missing (NM) versions of similarity (WS-353-(S)) and relat- edness (WS-353-(R)) based subsets of the WS-353 dataset. 94 3.7 Performance of the semantic measures on three biomedical datasets: MiniMayo, MeSH and MayoMeSH datasets. 96 4.1 Performance comparison of the DCRM measure with the previously best measure WikiSim on domain independent datasets. Bold values indicate the best correlation-based performance of any measure on a specific dataset. 114 5.1 Performance comparison of four classifiers using the hybrid model based on the 3-feature combination (f123). 125 5.2 A comparison of averaged feature ranking on all datasets. The highest rank corresponds to the lowest correlation-based performance of a feature and vice versa. 129 5.3 Performance comparison of both hybrid models with pre- viously best performing approaches on three benchmark datasets: M&C, R&G and WS-353. 130 6.1 Performance comparison of symmetric and asymmetric as- sociation based measures on two subsets of WS-353: WS353- Sim and WS353-Rel using proximity window with w = 10.. 146 6.2 Performance comparison of APRM avg measure with exist- ing state-of-art measures on the MTURK-771 dataset. 148 7.1 The Performance of APRM on various word choice datasets. The best performance on each dataset is shown in bold. 161 7.2 Performance comparison of APRM with state-of-art approaches on the RDWP dataset. The best performance is shown in bold.162 7.3 The performance of APRM on the synonymy-based word choice task using two datasets: ESL and TOEFL. The best performance is shown in bold. 166 LIST OF TABLES ix 8.1 Examples of two types of COPA question format. 176 8.2 Accuracy-based performance of existing approaches on the COPA-test benchmark dataset. 180 8.3 Performance comparison of Wikipedia-based semantic as- sociation measures using window Size with w=10 on three benchmark datasets of COPA evaluation. 185 x LIST OF TABLES List of Figures 1.1 The growth statistics of English Wikipedia [214]. .5 1.2 Thesis layout and connectivity of main chapters. 14 2.1 Effect of outlier on the Pearson’s correlation computation . 32 2.2 The landscape of semantic association computation approaches 44 3.1 Wikipedia hyperlink structure . 72 3.2 Semantic orientation of the shared associates of two concepts car and Gasoline........................ 74 3.3 A set of Wikipedia features used by Article Comparer [140] . 75 3.4 The framework for computing the Overlapping Strength-based Relatedness (OSR) score for a given term pair. 76 3.5 The framework for computing the CPRel score of a given term pair. 78 3.6 Performance comparison of three variants of CPRel on non- missing versions of three datasets: M&C, R&G and WS-353 datasets.
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