从隐喻和转喻的视角看待人体词“nose”认知语义网络及启示

一词多义是语言中非常重要的语言现象,具有重要的研究价值。对典型人体词"nose"的一词多义的认知研究,不仅有利于揭示词义拓展的基本规律、词义拓展的认知机制,而且对于揭示认知主体的思维模式和认知特点也具有重要的意义。从认知的角度对作为修辞手段的隐喻和转喻进行了新的探索,指出二者不仅是重要的思维模式和认知手段,也是一词多义的认知机制。通过一词多义的词义网络,揭示出词义的拓展不是任意的,隐喻和转喻在词义拓展过程中是相互交织的,进一步证实语言不是自治的、封闭的系统,从而在一定程度上驳斥了索绪尔的语言"任意说"的观点。     

流行语“门”族词一词多义的转喻解读

一词多义现象是人类认知发展的结果,文章从转喻视角出发,分析了流行语“门”族词的一词多义现象,用转喻的三种方式解读了流行语“门”族词的一词多义产生的原因,并阐述了影响转喻发生的三大影响因素,进一步帮助人们认识语言的一词多义现象。     

隐喻理论在大学英语多义词教学中的应用

一词多义是普遍的语言现象,也是英语的一大特色。概念隐喻理论指出,一个词语诸多义项不是任意的关联,隐喻是词义变化的重要方式,是词义扩展的重要理据。教师帮助学习者了解多义词的基本意义和隐喻意义之间的关系,学习者对词汇多个义项的记忆将不再是孤立的,割裂的,从而大大减轻其记忆负担,激发词汇学习的兴趣,促进词汇习得。文章通过大量的隐喻实例,并借助意象和思维导图等思维工具论述如何将概念隐喻和意象图式具体运用于大学英语多义词的教学,希望对大学英语词汇教学提供一些启发和参考。     

军语定义的形成及语义规范

军事概念通过内涵定义、外延定义以及两者综合定义方式等途径确立了其在军事概念体系中的定位,产生了与之相对应的军事术语。在军语最重要的单义性特征之外,部分军语还存在着多义、同（近）义和异义现象,对这类现象的处理是军语语义规范化的主要任务。     

词义演变的转喻认知机制

转喻同隐喻一样不仅是一种修辞手段,更是一种普遍的思维方式,对语言概念的生成与加工有着重要的作用。一词多义普遍存在于各种语言中。多义词义项之间有着有机的联系,构成一个语义范畴网络。通过举例,应用原型范畴及转喻的认知功能揭示多义词义项的延伸机理,对多义词各个义项之间的关系进行详细的阐释,为外语词汇的习得提供了一条有效的思路。从转喻入手学习词汇符合认知规律,可有效突破传统词汇学习的瓶颈,帮助学生达到真正意义上的概念流利。     

AVS体系在形名语义组合中的运用和发展

形容词与名词的语义组合,一般采用AVS体系来进行计算。Smith、Blutner和Frank的研究分别解决了组合中的典型性、语境性和否定性。为了进一步研究形容词的多义性,我们把系统中的值与多个特征联接起来。新提出的形容词概念语义模型能够更加有效地对汉语形容词进行释义,并解释相关的形名语义组合,这扩展了AVS体系的解释力。同时,应用模板自动获取这种AVS的想法在研究中被证明是可行的。     

Determining Prepositional Attachment, Prepositional Meaning, Verb Meaning, and Thematic Roles

An algorithm for semantic interpretation that integrates the determination of the meaning of verbs, the attachment and meaning of prepositions, and the determination of thematic roles is presented. The parser does not resolve structural ambiguity, which is solely the task of the semantic interpreter. Lexical semantic information about nouns and verbs is applied to the resolution of verb polysemy and modifier attachment. Semantic interpretation is centered on the representation of the meaning of the verb, called verbal concept. Verbal concepts are organized into a classification hierarchy. As long as the meaning of the verb remains unknown, parsing proceeds on a syntactic basis. Once the meaning of the verb is recognized, the semantic component makes sense of the syntactic relations built so far by the parser and of those still to be parsed. The algorithm has been implemented and tested on real–world texts.     

A method for disambiguating word senses in a large corpus

Word sense disambiguation has been recognized as a major problem in natural language processing research for over forty years. Both quantitive and qualitative methods have been tried, but much of this work has been stymied by difficulties in acquiring appropriate lexical resources. The availability of this testing and training material has enabled us to develop quantitative disambiguation methods that achieve 92% accuracy in discriminating between two very distinct senses of a noun. In the training phase, we collect a number of instances of each sense of the polysemous noun. Then in the testing phase, we are given a new instance of the noun, and are asked to assign the instance to one of the senses. We attempt to answer this question by comparing the context of the unknown instance with contexts of known instances using a Bayesian argument that has been applied successfully in related tasks such as author identification and information retrieval. The proposed method is probably most appropriate for those aspects of sense disambiguation that are closest to the information retrieval task. In particular, the proposed method was designed to disambiguate senses that are usually associated with different topics.William Gale is in a statistics department at AT&T Bell Laboratories. He has done research in physics, radio astronomy, and economics in the past, and founded the Society for Artificial Intelligence and Statistics. His current interests include lexical issues such as word sense discrimination, word similarity measures, and word correspondences in parallel texts.Kenneth Ward Church received his Ph.D. in Computer Science from MIT, and then went to work at AT&T Bell Laboratories on problems in speech and language. Recently, he has been advocating the use of statistical methods for analyzing large corpora.David Yarowsky is currently pursuing a Ph.D. in Computer Science at the University of Pennsylvania. He spent several years at AT&T Bell Laboratories doing research in statistical natural language processing.     

中日相同汉字不同词义的词汇比较

日语看似简单,特别是有些汉字在书写上与汉语相同,容易使人误认为用法也会相同。其实并不尽然,有些甚至完全不同。因此,了解和掌握中日同形字的异同点,并正确加以区分和使用非常重要。本文将对其进行分析和比较。     

基于互联网语料库的多义词“go”的词义扩展机制探究

在英语中，多义词“go”是最为活跃的词之一，其词汇意义呈多义性。本文借助互联网语料库对其多义性进行阐释。其词义扩展机制主要体现在三方面：第一，词汇本身意义的扩展；第二，词性的转变；第三，词汇意义的虚化。