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简·奥斯丁在她的代表作《傲慢与偏见》中,以班纳特一家为典型,生动细腻地描写了彭伯里庄园和尼日斐庄园里的各种人物的婚姻关系。奥斯丁用戏剧化的手法和反讽的语调向人们揭示了资产阶级婚姻中的“经济学”原理——金钱交易和利益的结合。 相似文献
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简·奥斯汀是英国18世纪最杰出的作家之一。《傲慢与偏见》是她的代表作。在作者所有的作品中,这部著作堪称最富于喜剧色彩,四处洋溢着“机智和幽默”。拟从《傲慢与偏见》人物的喜剧性格,人物的喜剧性对白,情节的喜剧性处理上来欣赏作家独特的讽刺艺术。 相似文献
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何樟桧 《大科技．科学之谜》2013,(1):47
《傲慢与偏见》是英国著名女作家简·奥斯汀的处女作,它讲述了乡村姑娘伊丽莎白与贵族青年达西之间的浪漫爱情故事。只不过,他们的爱情并非水到渠成,相反,因为青年的傲慢与姑娘的偏见,两个人差点错过彼此,直到误会消除,他们才真正地走到了一起。在这个故事当中,我们发现,伊丽莎白对达西的偏见几乎是从一开始就存在的。很多人认为,是 相似文献
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从《傲慢与偏见》和《简·爱》中爱情和婚姻的分析入手,探讨三种不同的婚姻在小说中的体现,建立在爱情基础上的婚姻,物质基础上的婚姻以及盲目基础上的婚姻分别带来的结局,以期引起当今青年人对婚姻的思考。 相似文献
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从《傲慢与偏见》和《简·爱》中爱情和婚姻的分析入手,探讨三种不同的婚姻在小说中的体现,建立在爱情基础上的婚姻,物质基础上的婚姻以及盲目基础上的婚姻分别带来的结局,以期引起当今青年人对婚姻的思考。 相似文献
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奥斯丁在《傲慢与偏见》中,通过在全知视角下具有限制性的叙述模式向我们展示了灰姑娘的浪漫爱情故事,描绘出作者所处时代的社会道德风貌,表达了作者的女性抗争意识及对当时社会婚姻观的批判。 相似文献
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简·奥斯丁是英国19世纪现实主义作家,她独特的创作风格和反讽艺术受到了西方文学评论界的高度评价。简·奥斯丁生活在一个舒适的安逸的环境中,接触到的是农村中产阶级、牧师等人物,她以女性的敏锐和细腻感觉描绘周围小小的世界,尤其是绅士淑女间的婚姻和爱情风波。奥斯丁的小说破旧立新,一反常规地展现了当时尚未受到资本主义工业革命冲击的英国乡村中产阶级的日常生活和庄园风光。本论文主要研究了她的文学背景,分析了她在小说《傲慢与偏见》中创作风格和艺术,同时指出了她对英国小说发展的杰出贡献。 相似文献
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班内特先生是人们在阅读《傲慢与偏见》时常常忽略的一个人物,然而在笔者看来,其语言犀利、幽默、睿智,折射出其鲜明的性格特征。本文试从言语行为叙述的运用、会话合作原则的有意违背两方面分析其话语,并据此分析其性格特征。最后发现班内特先生虽然话语不多,却不失为生活中一位难得的智者。 相似文献
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论文在目的论的基础上,从不同文本类型入手,分析了2005年版电影《傲慢与偏见》中贝奈特先生的语言以及相应的字幕翻译,希望能对贝奈特先生这一人物的刻画以及字幕翻译理论的研究带来一些启示。 相似文献
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The present paper,by the novel Pride and Prejudice,shows Jane Austen's opinion of marriage.And from which we also can see the executive concern with money of relationship in the English society at that time.Marriage,a special relationship,is also based on money.We all know that love is the basis of marriage,but at that time,in such a historical social environment,a good eco-nomic situation is necessary. 相似文献
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《傲慢与偏见》是英国著名女作家简.奥斯汀的一部力作,描述的是青年男女恋爱与婚姻。本文作者试通过分析小说四起典型的婚姻来讨论性格是如何影响一个人的婚姻价值定位的,以及简.奥斯汀对待婚姻的看法;《金粉世家》中的男女主人公也是存在类似的命运,真挚的爱情受到世俗偏见的考验,爱情的力量是伟大的,二人很幸福地结合了,然而最终作者却是以悲剧结局了整部作品。两部作品的爱情与婚姻相同而又具有差异性。 相似文献
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Shiwei Zhang Xiuzhen Zhang Jeffrey Chan Paolo Rosso 《Information processing & management》2019,56(5):1633-1644
Irony as a literary technique is widely used in online texts such as Twitter posts. Accurate irony detection is crucial for tasks such as effective sentiment analysis. A text’s ironic intent is defined by its context incongruity. For example in the phrase “I love being ignored”, the irony is defined by the incongruity between the positive word “love” and the negative context of “being ignored”. Existing studies mostly formulate irony detection as a standard supervised learning text categorization task, relying on explicit expressions for detecting context incongruity. In this paper we formulate irony detection instead as a transfer learning task where supervised learning on irony labeled text is enriched with knowledge transferred from external sentiment analysis resources. Importantly, we focus on identifying the hidden, implicit incongruity without relying on explicit incongruity expressions, as in “I like to think of myself as a broken down Justin Bieber – my philosophy professor.” We propose three transfer learning-based approaches to using sentiment knowledge to improve the attention mechanism of recurrent neural models for capturing hidden patterns for incongruity. Our main findings are: (1) Using sentiment knowledge from external resources is a very effective approach to improving irony detection; (2) For detecting implicit incongruity, transferring deep sentiment features seems to be the most effective way. Experiments show that our proposed models outperform state-of-the-art neural models for irony detection. 相似文献
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作为一种修辞手法,"反讽"已越来越多地应用于文学作品和人们的日常谈话中。本文作者试图在语言哲学的视角下,以质量准则、礼貌性和真诚性为基本参数,对日常言语行为中的"反讽"话语加以分析,以期读者能够更好地理解"反讽"话语中的哲学含义。 相似文献
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克雷洛夫的寓言内容深刻,发人深省,且多以俄罗斯的社会生活作为题材,通过对典型形象的塑造,反映生活的本质,表达对沙皇及其官僚的嘲讽,对封建农奴制度的抨击,对现实生活中丑恶现象的批判,对生活的评判以及对人生哲理的阐释。 相似文献
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Ekaterina Pronoza Polina Panicheva Olessia Koltsova Paolo Rosso 《Information processing & management》2021,58(6):102674
Ethnicity-targeted hate speech has been widely shown to influence on-the-ground inter-ethnic conflict and violence, especially in such multi-ethnic societies as Russia. Therefore, ethnicity-targeted hate speech detection in user texts is becoming an important task. However, it faces a number of unresolved problems: difficulties of reliable mark-up, informal and indirect ways of expressing negativity in user texts (such as irony, false generalization and attribution of unfavored actions to targeted groups), users’ inclination to express opposite attitudes to different ethnic groups in the same text and, finally, lack of research on languages other than English. In this work we address several of these problems in the task of ethnicity-targeted hate speech detection in Russian-language social media texts. This approach allows us to differentiate between attitudes towards different ethnic groups mentioned in the same text – a task that has never been addressed before. We use a dataset of over 2,6M user messages mentioning ethnic groups to construct a representative sample of 12K instances (ethnic group, text) that are further thoroughly annotated via a special procedure. In contrast to many previous collections that usually comprise extreme cases of toxic speech, representativity of our sample secures a realistic and, therefore, much higher proportion of subtle negativity which additionally complicates its automatic detection. We then experiment with four types of machine learning models, from traditional classifiers such as SVM to deep learning approaches, notably the recently introduced BERT architecture, and interpret their predictions in terms of various linguistic phenomena. In addition to hate speech detection with a text-level two-class approach (hate, no hate), we also justify and implement a unique instance-based three-class approach (positive, neutral, negative attitude, the latter implying hate speech). Our best results are achieved by using fine-tuned and pre-trained RuBERT combined with linguistic features, with F1-hate=0.760, F1-macro=0.833 on the text-level two-class problem comparable to previous studies, and F1-hate=0.813, F1-macro=0.824 on our unique instance-based three-class hate speech detection task. Finally, we perform error analysis, and it reveals that further improvement could be achieved by accounting for complex and creative language issues more accurately, i.e., by detecting irony and unconventional forms of obscene lexicon. 相似文献
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【目的/意义】探究针对微博文本的基于深度学习的情绪分类有效方法,研究微博热点事件下用户转发言论的情绪类型与隐私信息传播的关系。【方法/过程】选用BERT、BERT+CNN、BERT+RNN和ERNIE四个深度学习分类模型设置对比实验,在重新构建情绪7分类语料库的基础上验证性能较好的模型。选取4个微博热点案例,从情绪分布、情感词词频、转发时间和转发次数四个方面展开实证分析。【结果/结论】通过实证研究发现,用户在传播隐私信息是急速且短暂的,传播时以“愤怒”和“厌恶”等为代表的消极情绪占主导地位,且会因隐私信息主体的不同而产生情绪类型和表达方式上的差异。【创新/局限】研究了用户在传播隐私信息行为时的情绪特征及二者的联系,为保护社交网络用户隐私信息安全提供有价值的理论和现实依据,但所构建的语料库数据量对于训练一个高准确率的深度学习模型而言还不够,且模型对于反话、反讽等文本的识别效果不佳。 相似文献
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基于甘肃黄土高原区12个测站1960-2012年的气象资料,利用标准化降水蒸散指数对该区近53a四季干旱的时空变化特征进行了分析。结果表明：甘肃黄土高原区近53a的四季均呈干旱化趋势,以秋季最为突出,春季次之。经过Mann-Kendall趋势分析法检验,秋旱最显著的地方是兰州、靖远及环县;春旱最明显的地方是崆峒、庆阳和西峰;冬季有变湿的趋向,但未通过显著性水平。空间分布上,甘肃黄土高原区腹部比边缘区发生干旱的频率高,山地比谷地发生干旱的频率高。陇中地区夏旱频发,陇东地区冬旱频繁。干旱变幅最大的地方是研究区西北的乌鞘岭和东北的子午岭。近53a研究区干旱事件发生频次呈增长趋势,近10a来尤为显著。中度干旱发生频次的增长速度比极端干旱事件增长的快,但没有极端干旱事件显著。 相似文献