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Chapter 1 Introduction

1.1 Background of the study
Word sense disambiguation (WSD) is the task to identify the intended meaning of anambiguous word in context in a computational manner (Navigli, 2009). It has been one ofthe most important issues in the field of natural language processing and semantics. It hasbeen considered in almost every application of natural language processing, includingmachine translation, information retrieval, information extraction, knowledgeacquisition/mining, semantic interpretation and lexicography, and is becomingincreasingly important in new research areas such as bioinformatics and the Semantic Web.In general, there are four conventional approaches to WSD, namely dictionary- andknowledge-based methods, supervised methods, semi-supervised or minimally-supervisedmethods and unsupervised methods (Agirre & Edmonds, 2006). Many scholars at homeand abroad have made remarkable progress on the study of the WSD with those methods.Yarowsky (1995) has presented an unsupervised learning algorithm for sensedisambiguation. Navigli and Velardi (2005) have demonstrated the structural semanticinterconnections algorithm, a structural pattern matching algorithm for WSD. Liu, Huangand Jiang (2006) have presented an approach based on supervised AdaBoost MH learningalgorithm for Chinese WSD. Hristea, Popescu and Dumitrescu (2008) have showed a newmethod of WSD which is between unsupervised and knowledge-based techniques forword sense disambiguation.
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1.2 Aim and significance of the study
The research of this paper mainly focuses on two fields: the WSD and theprepositions. Therefore, this paper has the following significances.First, from the perspective of prepositions, it can enrich the semantic study ofprepositions and can help us deeply understand the fundamental knowledge behind thesenses of prepositions. Meanwhile, it can provide the theoretical and practical study forother semantically complex prepositions. 3Second, from the perspective of the WSD, it is designed to provide valuablelinguistic information to the researches of the WSD in natural language processing. Thestudy of the WSD of English prepositions is crucial to semantic study and the naturallanguage processing.Third, from the perspective of research method, formal concept analysis is a newmethod to the study of English prepositions. It can make the research more objective andrational.This paper aims to realize the WSD of English preposition over by the approach andtheory of formal concept analysis (FCA). Based on the WSD model of over, it tries toprobe into the interactions of contextual features that influence the senses of Englishproposition over by interpreting structural partial-order attribute diagram (SPAOD). Theinteractions of contextual features are discussed from three aspects: the layer distributionof contextual features, the influence of different contextual features on the senses of overand the intents and extents of the senses of over. By analysis, this paper endeavors touncover the underlying knowledge behind English preposition over.1.3 Creative pointsThis paper studies the WSD of English preposition over from the
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Chapter 2 Literature Review

2.1 Previous studies on English prepositions
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