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Automatic Extraction of Examples for Word Sense Disambiguation

Automatic Extraction of Examples for Word Sense Disambiguation

Acknowledgements This

Acknowledgements This thesis would not have been possible unless Professor Dr. Walt Detmar Meurers provided me the opportunity to complete my work at the Department of Linguistics at the University of Tübingen. The valuable guidance and professional environment in the department ensured also by my second supervisor Dr. Dale Gerdemann contributed immensely to my final success. I would like to express as well my extreme gratitude to my advisor Professor Dr. Sandra Kübler for the constant and exceptionally constructive remarks, which were often the key to my progress. I am also grateful to all those who provided assistance in numerous ways: David Münzing and Julian Münzing for the manual sense-annotation of a subset of my final data collection - a very laborious endeavor; Franziska Gruber, Ramon Ziai, Dominikus Wetzel and my loving family and friends for their endless support, encouragement and advice. 3

Contents 1 Introduction 10 2 Basic Approaches to Word Sense Disambiguation 12 2.1 Knowledge-Based . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.1.1 The Lesk Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.1.2 Alternative Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2 Unsupervised Corpus-Based . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2.1 Distributional Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2.2 Translational Equivalence Methods . . . . . . . . . . . . . . . . . . . . . . . 16 2.3 Supervised Corpus-Based . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.3.1 Sense Inventories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.3.2 Source Corpora . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.3.3 Data Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.3.4 Feature Vectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.3.5 Supervised WSD Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.4 Semi-Supervised Corpus-Based . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3 Comparability for WSD Systems 32 3.1 Differences between WSD Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.2 Most Frequently Used Baselines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.2.1 The Lesk Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.2.2 Most Frequent Sense . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.2.3 Random Choice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 4 Evaluation of WSD Systems 35 4.1 Fundamentals in Evaluation of WSD Systems . . . . . . . . . . . . . . . . . . . . . 35 4.2 International Evaluation Exercise Senseval . . . . . . . . . . . . . . . . . . . . . . . 37 4

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