Idioms are a very important part of the English language: you are told that if you want to go far (succeed) you should pull your socks up (make a serious effort to improve your behaviour, the quality of your work, etc.) and use your grey matter (brain).1 Learning and translating idioms have always been very difficult for foreign language learners. The present paper explores some of the reasons why English idiomatic expressions are difficult to learn and translate. It is not the aim of this paper to attempt a comprehensive survey of the vast amount of material that has appeared on idioms in Adams and Kuder (1984), Alexander (1984), Dixon (1983), Kirkpatrick (2001), Langlotz (2006), McCarthy and O'Dell (2002), and Wray (2002), among others. The paper concentrates on idioms as a learning-translation problem; it makes no claim to be comprehensive or academically rigorous. Leech (1989) defines an idiom as follows: “An idiom is a group of two or more words which we have to treat as a unit in learning a language. We cannot arrive at the meaning of the idiom just by adding together the meanings of the words inside it. E.g.John and Mary usedto be hardup (='They had very little money'.)”(P.186) To be more exact, an idiom is a sequence of words which is semantically and syntactically restricted, so that they function as a single unit. From a semantic point of view, the meanings of the individual words cannot be summed to produce the meanings of the idiomatic expression as a whole. Thus, fly off the handle, which means lose one's temper, cannot be understood in terms of the meanings of fly, off, or handle. The idiom phrase hot air, which means empty or boastful talk, is neither hot nor air; with hot air we are dealing with a set phrase where the meaning cannot be suggested on the basis of the two constituent words. The idiomatic meaning of spill the beans in So who spilt the beans (=told the secret) about her affair with David? has nothing to do with beans or with spilling in its literal sense. The foreign-language learner is left trying to figure out where and how the beans were spilt. From a syntactic viewpoint, the constituent parts of an idiom often do not permit the usual variability they display in other contexts. The point to be emphasized here is this: most idioms do not lend themselves easily to manipulation by speakers and writers; they are invariable and must be learned as wholes, but concord ofnumber, person and gender in the idiom phrase is still necessary, i.e. the verbs must be put into the correct form, and pronouns must agree with their antecedents: I don't give a hoot for her opinion! 2 • She doesn't give a hoot for my opinion! etc.)║He won, but only by the skin of his teeth2• She won, but only by the skin of her teeth• Iwon, but only by the skin ofmy teeth,I had to run for the train, and caught it by the skin of myteeth, etc.║He kept pullingmy arm, throwing me off my balance 2 • She kept pulling his arm, throwing him off his balance • We kept pullingher arm, throwingher offher balance, etc.2 The present paper is divided into five parts, as follows: Part I: An Overview; PART II: Learner’s Difficulties with Idioms; PART III: Some Pedagogical recommendations and Suggestions about Idioms; Part IV: Activities to Practice Idiomatic Expressions; Part V: Summary and Conclusion.
يمثل الأخذ بالنظام الفيدرالي أطاراً تنظيمياً لشكل الدولة و مرحلة تحول مهمة في بنية الدولة العامة في مختلف مجالاتها، فالانتقال من المركزية في أدارة الشؤون العامة للدولة الى النمط الفيدرالي يمثل تحولا بنيوياً وسيكولوجياً ،حيث يكون هنالك توزيع مكاني - عمودي للسلطة والثروة بين الوحدات المكونة للدولة بشكل يختلف كليا عن الحالة المركزية، ونجد صور تنظيمية عديدة تتأسس ضمن اطار الفيدرالية العام ،
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(FEM) into a neural network structure using a simple two - dimensional problem. The structure of this neural network is described
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... Show MoreMany academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Deci
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