朴素贝叶斯语义排歧情态动词must语言特征.doc

朴素贝叶斯论文：基于朴素贝叶斯模型研究语言特征对情态动词MUST语义影响【中文摘要】语义排歧研究是自然语言处理领域的热门话题,而情态动词研究是语言学领域里具有相当历史性而且很重要的研究方向。因此,利用自然语言处理的方法研究情态动词的语义是一个跨学科的研究,而且具有重要的意义。本文采用语义排歧方法来探讨情态动词must的语义问题。本文采用语料库定性定量相结合的研究方法和朴素贝叶斯有导语义排歧方法,以Coates(1983)对情态动词的研究成果为基础进一步探讨了如下两个问题:第一,不同语言特征对情态动词must的语义影响如何;第二,哪些语言特征相组合能产生最佳排歧效果。本文的创新点可体现在如下几个方面:第一,实现了朴素贝叶斯方法对情态动词must的语义排歧,使得语义排歧研究突破了目前大多只排歧名词、动词、形容词等语义比较简单的词的局限,从而使语义排歧研究迈进到了情态动词层面;第二,通过间接性的使用互信息(MI)值,使得互信息(MI)值这个重要的语言特征能在朴素贝叶斯排歧方法中发挥作用;第三,语言特征选择过程中条件互信息的使用,使得本文结论更具有科学性。实验结果显示,不同语言特征对情态动词must的语义影响程度也不同,按影响程度由大到小的顺序排列为:情态动词后动词的完成体;.【英文摘要】Word Sense Disambiguation (WSD) is a hot topic of the field of Natural Language Processing (NLP), while the study of modality, which has a long history, is a very important branch of linguistic field. Therefore, the semantic study of modal verbs by the methods of NLP is a cross-disciplinary study and of great significance. This thesis adopts the WSD method to conduct a semantic investigation into the modal verb must.This thesis takes a corpus-based qualitative and quantitative research method. Based on .【关键词】朴素贝叶斯 语义排歧 情态动词must 语言特征【英文关键词】Na(i|)ve Bayes Word Sense Disambiguation modal verb must linguistic features【索购全文】联系Q1：138113721 Q2：139938848【目录】基于朴素贝叶斯模型研究语言特征对情态动词MUST语义影响摘要5-7Abstract7-8Chapter 1 Introduction15-201.1 Background of this study15-171.2 Significance and potential application of the present study17-181.3 Objectives of the present study181.4 Creative points of the present study181.5 Outline of the present study18-20Chapter 2 Literature Review20-332.1 Study of modality20-232.2 Study of WSD23-272.3 Study of NB in WSD27-302.3.1 Application of NB in WSD27-292.3.2 Explanation of the superb performance of NB29-302.4 Brief discussion about the previous studies30-322.5 Summary32-33Chapter 3 Research Method and Data Collection33-373.1 Research method33-353.2 Data collection35-37Chapter 4 WSD by Na?ve Bayesian Model37-654.1 Theoretical foundations37-504.1.1 Fundamental knowledge about WSD37-424.1.2 Theories on modality42-454.1.3 Coatesviews about modal verb Must45-474.1.4 Na?ve Bayes47-504.2 Design of NB WSD model50-544.2.1 Determination of the sense inventory504.2.2 Determination of the training information source for WSD mode50-514.2.3 Feature extraction from the context51-524.2.4 Determination of the WSD method52-534.2.5 Determination of the WSD algorithm53-544.2.6 Evaluation544.3 Experiments by NB WSD model54-644.3.1 Training of the NB WSD model54-604.3.2 Testing of the NB WSD model60-644.4 Summary64-65Chapter 5 Result Analysis and Discussion65-865.1 Result analysis65-745.1.1 Result analysis of the solution to the first problem65-725.1.2 Result analysis of the solution to the second problem72-745.2 Discussion74-855.2.1 Discussions about the conclusions of 5.174-805.2.2 Discussions about the corpuses in this study80-815.2.3 Discussions about the soundness of the indirect use of MI in this study81-825.2.4 Discussions about the feature of existential subject825.2.5 Discussions about the experiments of ranking features and finding the best feature combination82-835.2.6 Comparison with the results of previous related studies83-855.3 Summary85-86Chapter 6 Conclusion86-906.1 Main findings of the research86-876.2 Implica