情态动词will 语义排歧 支持向量机 人工神经网络 特征提取

情态动词情态动词 willwill 论文 基于支持向量机的英语情态动词论文 基于支持向量机的英语情态动词 WillWill 语义排歧研究语义排歧研究 中文摘要 语义排歧是指根据目标词出现的上下文语境自动 识别其意义 语义排歧是机器翻译 信息检索 语音识别 文本分 类以及人机交互等诸多领域中的关键环节 是自然语言处理领域的热 点和难点 尽管语义排歧技术取得了很大发展 但目前的语义排歧研 究对象还是主要集中在普通动词和名词的语义排岐上 情态表达说 话人的态度和意见 主要由情态动词来实现 因此 正确识别情态动 词的语义对理解和领会说话人的态度和意见十分重要 情态动词语 义有三种不确定性 梯度 歧义和融合 这些不确定性使人们很难把 握其准确意义 因此 建立一个有效的 准确性较高的情态动词语义 排歧模型变得至关重要 本研究基于 120 万字的语料库 从 will 的 实际使用语境中提取八个语义特征和句法特征 并采用数据挖掘中的 一种新方法 支持向量机 建立了情态动词 will 的语义排歧模型 实验结果显示 由支持向量机方法建立的情态动词 will 的语义排歧 模型的排歧精度达到了 98 33 这个结果 证实了采用支持向量机 对情态动词 will 语义排歧的有效性 同时证明了从真实的语料库中 提取的 8 个语言特征的有效性 为了验证支持向量机语义排歧效果 的优越性 本文采用神经网络技术中的反向传播神经网络 径向基神 经网络 英文摘要 Word sense disambiguation is the task to identify the intended meaning of an ambiguous word in a certain context Due to its wide application in machine translation information retrieval speech recognition text categorization it has been one of the hot and tough issues in natural language processing Although techniques of word sense disambiguation have advanced greatly the research objects have mainly centered on the common nouns and verbs Modality is concerned with the speaker s opinion or attitude 关键词 情态动词 will 语义排歧 支持向量机 人工神经网 络 特征提取 英文关键词 modal verb will word sense disambiguation Support Vector Machines artificial neural network feature selection 索购全文索购全文 联系联系Q Q1 1 138113721138113721 Q Q2 2 139938848139938848 目录 基于支持向量机的英语情态动词 Will 语义排歧研究 摘要 5 7 Abstract 7 8 Abbreviations 12 13 List of Tables 13 14 List of Figures 14 15 Chapter 1 Introduction 15 19 1 1 Background of the present study 15 17 1 2 Objectives of the present study 17 1 3 Outline of the thesis 17 19 Chapter 2 Literature Review 19 37 2 1 Studies on the word sense disambiguation 19 25 2 1 1 Studies on the word sense disambiguation abroad 19 22 2 1 2 Studies on the word sense disambiguation in China 22 25 2 2 Studies on the application of support vector machines 25 31 2 2 1 Study on the application of support vector machines abroad 26 29 2 2 2 Study on the application of support vector machines in China 29 31 2 3 Studies on the English modality 31 35 2 4 Space for the present study 35 37 Chapter 3 Theoretical Foundation and Methodology 37 41 3 1 Theoretical foundation of the present study 37 38 3 2 Research method and data collection 38 39 3 3 Summary 39 41 Chapter 4 Semantic Categorization of the English Modal Verb Will 41 46 4 1 Why is will 41 42 4 2 Categorization of meanings of English modal verb will 42 44 4 3 Summary 44 46 Chapter 5 The Building of the WSD Model of Will by SVM 46 58 5 1 The working principle of support vector machines 46 48 5 2 Selection of training samples and test samples 48 49 5 3 Construction of feature sets 49 51 5 4 Vectorization of the linguistic features 51 52 5 5 The building of WSD model by SVM 52 57 5 5 1 Training and testing with default parameters 52 54 5 5 2 Training and testing with optimal parameters 54 56 5 5 3 Model selection 56 57 5 6 Summary 57 58 Chapter 6 Comparative Analysis of the Models of WSD Models by SVM and by ANN 58 73 6 1 The working principle of artificial neural networks 58 60 6 2 The building of the WSD model by BP RBF and PNN 60 65 6 3 Comparative analysis of the four WSD models of will 65 68 6 4 Analysis and discussion on the misclassified samples 68 72 6 5 Summary 72 73 Chapter 7 Contributions of Different Linguistic Features to the WSD of Will 73 88 7 1 The contributions of the semantic features to the WSD of will 75 80 7 2 The contributions of syntactic features to WSD of will 80 82 7 3 The contribution of each linguistic feature to the WSD of will 82 84 7 4 Validation of the importance of linguistic features to the WSD of will 84 86 7 5 Summary 86 88 Chapter 8 Conclusions 88 91 References 91 103 A