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.
The Incentives legislation aims to raise the efficiency of job performance of all kinds through optimal investment of human resources and their capabilities to raise or increase and production influence and provide distinguished and creative services such as The Incentives Law of Productive Ministries Employees No. 20 of 1993 and instructions issued by ministries and relevant Facilities regarding incentives for their affiliates based on provisions Public Companies Law and the instructions of the Higher Education Fund issued by the Ministry of Higher Education and Scientific Research. The human element is the most unstable and complex element of production as it is characterized by a set of feelings and emotions that is expresses
... Show MoreBackground: The aim of this in vitro study was to evaluate and compare the effect of preheating microleakage among three different filler size composites which include Filtektm Z250 micro hybrid, Z250xt Nano hybrid and nanocomposite Z350xt. in Class II cavity preparation .
Materials and methods: sixty maxillary first premolars were prepared with class II cavities. Samples were divided into three groups according to material used group A (FiltekZ250 micro hybrid). Group B(Z250xt Nano hybrid). Group C (nanocomposite Z350xt)and each group divided into two subgroups of ten teeth according to temperature of composite:
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QJ Rashid, IH Abdul-Abbas, MR Younus, PalArch's Journal of Archaeology of Egypt/Egyptology, 2021 - Cited by 4
Estimating an individual's age from a photograph of their face is critical in many applications, including intelligence and defense, border security and human-machine interaction, as well as soft biometric recognition. There has been recent progress in this discipline that focuses on the idea of deep learning. These solutions need the creation and training of deep neural networks for the sole purpose of resolving this issue. In addition, pre-trained deep neural networks are utilized in the research process for the purpose of facial recognition and fine-tuning for accurate outcomes. The purpose of this study was to offer a method for estimating human ages from the frontal view of the face in a manner that is as accurate as possible and takes
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Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi
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Sentiment analysis is one of the major fields in natural language processing whose main task is to extract sentiments, opinions, attitudes, and emotions from a subjective text. And for its importance in decision making and in people's trust with reviews on web sites, there are many academic researches to address sentiment analysis problems. Deep Learning (DL) is a powerful Machine Learning (ML) technique that has emerged with its ability of feature representation and differentiating data, leading to state-of-the-art prediction results. In recent years, DL has been widely used in sentiment analysis, however, there is scarce in its implementation in the Arabic language field. Most of the previous researches address other l
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Background: The efficacy of educational strategies is crucial for nursing students to competently perform pediatric procedures like nasogastric tube insertion. Specific Background: This study evaluates the effectiveness of simulation, blended, and self-directed learning strategies in enhancing these skills among nursing students. Knowledge Gap: Previous research lacks a comprehensive comparison of these strategies' impacts on skill development in pediatric nursing contexts. Aims: The study aims to assess the effectiveness of different educational strategies on nursing students' ability to perform pediatric nasogastric tube insertions. Methods: A pre-experimental design was employed at the College of Nursing, University of Baghdad, i
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Gender classification is a critical task in computer vision. This task holds substantial importance in various domains, including surveillance, marketing, and human-computer interaction. In this work, the face gender classification model proposed consists of three main phases: the first phase involves applying the Viola-Jones algorithm to detect facial images, which includes four steps: 1) Haar-like features, 2) Integral Image, 3) Adaboost Learning, and 4) Cascade Classifier. In the second phase, four pre-processing operations are employed, namely cropping, resizing, converting the image from(RGB) Color Space to (LAB) color space, and enhancing the images using (HE, CLAHE). The final phase involves utilizing Transfer lea
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