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Titlebook: Inner Speech - L2; Thinking Words in a María C.M. Guerrero Book 2005 Springer-Verlag US 2005 Cultural Theory.Index.first language.gender.l

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書(shū)目名稱(chēng)Inner Speech - L2
副標(biāo)題Thinking Words in a
編輯María C.M. Guerrero
視頻videohttp://file.papertrans.cn/467/466610/466610.mp4
概述Only book devoted wholly to the topic of "inner speech in the second language." It facilitates the work of researchers who are interested in the inner speech-L2 connection inasmuch as it gathers, upda
叢書(shū)名稱(chēng)Educational Linguistics
圖書(shū)封面Titlebook: Inner Speech - L2; Thinking Words in a  María C.M. Guerrero Book 2005 Springer-Verlag US 2005 Cultural Theory.Index.first language.gender.l
描述According to Vygotsky (1986), The decreasing vocalization of egocentric speech denotes a developing abstraction from sound, the child‘s new faculty to "think words" instead of pronouncing them. This is the positive meaning of the sinking coefficient of egocentric speech. The downward curve indicates development toward inner speech, (p. 230) The purpose of this volume is to explore the faculty to "think words," not as the ability to mentally evoke words in the native (or first) language (LI) but as the faculty 1 to conjure up in the mind words in a second language (L2). To think words rather than to pronounce them is possible through inner speech, a function that humans develop in the course of childhood as they internalize the speech of the social group among which they grow. This means internalizing and being able to conduct inner speech in a particular linguistic code, the LI. But humans, at a very early or more mature age, may also come into contact and interact verbally with speakers of other languages, in classrooms or natural settings. The possibility thus emerges of internalizing an L2 in such a way that inner speech in the L2 might evolve. In this book, it is argued that, g
出版日期Book 2005
關(guān)鍵詞Cultural Theory; Index; first language; gender; language; nature; second language; subject
版次1
doihttps://doi.org/10.1007/b106255
isbn_softcover978-1-4419-3754-4
isbn_ebook978-0-387-24578-2Series ISSN 1572-0292 Series E-ISSN 2215-1656
issn_series 1572-0292
copyrightSpringer-Verlag US 2005
The information of publication is updating

書(shū)目名稱(chēng)Inner Speech - L2影響因子(影響力)




書(shū)目名稱(chēng)Inner Speech - L2影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Inner Speech - L2網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Inner Speech - L2網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Inner Speech - L2被引頻次




書(shū)目名稱(chēng)Inner Speech - L2被引頻次學(xué)科排名




書(shū)目名稱(chēng)Inner Speech - L2年度引用




書(shū)目名稱(chēng)Inner Speech - L2年度引用學(xué)科排名




書(shū)目名稱(chēng)Inner Speech - L2讀者反饋




書(shū)目名稱(chēng)Inner Speech - L2讀者反饋學(xué)科排名




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c summary, machine translation, dialogue system, etc. All of them need to use high-quality keywords as a starting point. In this paper, we propose a deep learning network called deep neural semantic network (DNSN) to solve the problem of short text keyword extraction. It can map short text and words
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main word segmentation algorithms: dictionary-based word segmentation algorithms, statistics-based word segmentation algorithms, and understanding-based word segmentation algorithms. However, few people combine these three methods or two of them. Therefore, a Chinese word segmentation model is propo
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