基于语料库的英语同义词语义框架个案研究
发布时间：2018-07-16 20:19
【摘要】：英语词汇中具有丰富的同义词,其准确辨析与恰当运用一直是中国英语学习者学习中的一大难点。传统的同义词辨析一般借助于相关词典的释义和例句,因此具有很大的局限性。基于语料库的研究方法为同义词更加直观、全面的辨析提供了可能。传统的辨析方法与基于语料库方法的结合可以使英语同义词辨析变得更为合理有效。国外对基于语料库的同义词研究主要集中在两个方面:英语同义词在本族语语料库中的特征研究及两种不同语言中对应同义词的特征研究。国内除研究英语同义词在本族语语料库中的特征外,也侧重于对中国英语学习者对其同义词的使用进行探讨。国内外学者通常从四个方面对此领域进行探索:语域、类联接、搭配和语义韵,但他们很少提及具体的理论去支撑其研究,从潜在语义的角度对英语同义词进行辨析的研究则极为欠缺。同义词的语义差异来源于贮存在人脑中的背景知识和经验的差异。因此,本研究将在框架语义理论的指导下,基于英国国家语料库(BNC),借助三个自然语言处理软件WordNet、Sketch Engine及Latent Semantic Analysis @ CU Boulder对十组英语同义词(五组动词同义词和五组形容词同义词)从四个方面进行辨析:类联接、搭配、语义韵和潜在语义,旨在为学习者准确使用英语同义词提供新的方法与依据。研究结果表明,英语同义词在类联接上的差异主要体现在两个方面:类联接模式及每个模式的使用频率,且在大多数情况下,动词同义词和形容词同义词后的搭配介词能够成为区分这组同义词的显著标志。从搭配来说,同义词在其共同搭配模式和唯一搭配模式中都存在差异,且这两种模式下得出的同义词辨析结果具有很大的一致性。任何一组同义词在语义韵上都存在差别,有的具有明显不同的语义韵;有的即使具有相同的语义韵,但在每种语义韵所占的比例上也会有不同。每组同义词的潜在语义相似值都不同;且通过比较同义词所在句子之间连贯性的平均值可得出:搭配词的意义较为集中的同义词所在句子连贯性的平均值要比搭配词的意义较为分散的同义词所在句子的连贯性的平均值高。英语同义词在以上四方面的差异都可以用其相关的背景知识与经验去解释。本研究建议教师应适当地将基于语料库的同义词辨析方法运用到课堂上,使学生能够在大量真实的语料中学会全面分析与理解同义词;同时,教师应加强对学生的词汇背景知识的输入,分析不同同义词的语义框架,同时鼓励学生通过广泛阅读、通过与英语母语者交流等方式正确运用词汇。
[Abstract]:There are abundant synonyms in English vocabulary. It is always a difficulty for Chinese English learners to distinguish and use them correctly. The traditional synonym discrimination is generally based on the interpretation and examples of relevant dictionaries, so it has great limitations. Corpus-based research provides the possibility for synonyms to be more intuitive and comprehensive. The combination of traditional discriminating method and corpus-based method can make English synonym discrimination more reasonable and effective. The research on the synonyms based on corpus abroad mainly focuses on two aspects: English synonyms in the native language corpus and the corresponding synonyms in two different languages. Besides studying the features of English synonyms in the native language corpus, the author also focuses on the use of their synonyms by Chinese English learners. Scholars at home and abroad usually explore this field from four aspects: register, class join, collocation and semantic prosody, but they seldom mention specific theories to support their research. The research on the discrimination of English synonyms from the perspective of latent semantics is extremely lacking. Semantic differences of synonyms come from differences in background knowledge and experience stored in the human brain. Therefore, this study will be guided by the frame semantic theory. Based on British National Corpus (BNC), ten groups of English synonyms (five sets of verb synonyms and five sets of adjective synonyms) are analyzed from four aspects with the help of three natural language processing software-WordNet Sketch engine and Latent semantic Analysis @ CU Boulder. Semantic prosody and latent semantics are intended to provide new methods and bases for learners to use English synonyms accurately. The results show that the differences of English synonyms in class join are mainly reflected in two aspects: the mode of class join and the frequency of use of each pattern, and in most cases, The collocation prepositions after verb synonyms and adjective synonyms can be marked to distinguish these synonyms. In terms of collocation, synonyms are different in their common collocation mode and unique collocation mode, and the results of synonym discrimination under these two models are very consistent. Any group of synonyms are different in semantic prosody, some have distinct semantic prosody, some even have the same semantic rhyme, but the proportion of each kind of semantic rhyme will also be different. The potential semantic similarity values of each group of synonyms are different; By comparing the average of the coherence between the synonyms, we can conclude that the average of the sentence coherence of the synonyms with the more concentrated meaning of collocation words is higher than that of the synonyms with more scattered meaning of collocation words. The average of coherence is high. The differences of English synonyms in the above four aspects can be explained by their relevant background knowledge and experience. The present study suggests that teachers should properly apply the corpus based synonym discrimination method to the classroom, so that students can learn to analyze and understand synonyms comprehensively in a large number of real corpus; at the same time, Teachers should strengthen the input of students' vocabulary background knowledge, analyze the semantic framework of different synonyms, and encourage students to use vocabulary correctly through extensive reading and communication with native English speakers.
【学位授予单位】：广西大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：H313
本文编号：2127566
[Abstract]:There are abundant synonyms in English vocabulary. It is always a difficulty for Chinese English learners to distinguish and use them correctly. The traditional synonym discrimination is generally based on the interpretation and examples of relevant dictionaries, so it has great limitations. Corpus-based research provides the possibility for synonyms to be more intuitive and comprehensive. The combination of traditional discriminating method and corpus-based method can make English synonym discrimination more reasonable and effective. The research on the synonyms based on corpus abroad mainly focuses on two aspects: English synonyms in the native language corpus and the corresponding synonyms in two different languages. Besides studying the features of English synonyms in the native language corpus, the author also focuses on the use of their synonyms by Chinese English learners. Scholars at home and abroad usually explore this field from four aspects: register, class join, collocation and semantic prosody, but they seldom mention specific theories to support their research. The research on the discrimination of English synonyms from the perspective of latent semantics is extremely lacking. Semantic differences of synonyms come from differences in background knowledge and experience stored in the human brain. Therefore, this study will be guided by the frame semantic theory. Based on British National Corpus (BNC), ten groups of English synonyms (five sets of verb synonyms and five sets of adjective synonyms) are analyzed from four aspects with the help of three natural language processing software-WordNet Sketch engine and Latent semantic Analysis @ CU Boulder. Semantic prosody and latent semantics are intended to provide new methods and bases for learners to use English synonyms accurately. The results show that the differences of English synonyms in class join are mainly reflected in two aspects: the mode of class join and the frequency of use of each pattern, and in most cases, The collocation prepositions after verb synonyms and adjective synonyms can be marked to distinguish these synonyms. In terms of collocation, synonyms are different in their common collocation mode and unique collocation mode, and the results of synonym discrimination under these two models are very consistent. Any group of synonyms are different in semantic prosody, some have distinct semantic prosody, some even have the same semantic rhyme, but the proportion of each kind of semantic rhyme will also be different. The potential semantic similarity values of each group of synonyms are different; By comparing the average of the coherence between the synonyms, we can conclude that the average of the sentence coherence of the synonyms with the more concentrated meaning of collocation words is higher than that of the synonyms with more scattered meaning of collocation words. The average of coherence is high. The differences of English synonyms in the above four aspects can be explained by their relevant background knowledge and experience. The present study suggests that teachers should properly apply the corpus based synonym discrimination method to the classroom, so that students can learn to analyze and understand synonyms comprehensively in a large number of real corpus; at the same time, Teachers should strengthen the input of students' vocabulary background knowledge, analyze the semantic framework of different synonyms, and encourage students to use vocabulary correctly through extensive reading and communication with native English speakers.
【学位授予单位】：广西大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：H313
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