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The Vulgar Tongue: Green's History of Slang

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A comprehensive history of slang in the English speaking world by its leading lexicographer.


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The Universal Structure of Categories: Towards a Formal Typology

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This book presents a new theory of grammatical categories - the Universal Spine Hypothesis - and reinforces generative notions of Universal Grammar while accommodating insights from linguistic typology.


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Academic Paper


Title: Corpus-Based Translation Methods
Author: Iman Tohidian
Email: click here to access email
Institution: University of Kashan
Linguistic Field: Computational Linguistics
Abstract: Machine Translation (MT) is the oldest application area of computational linguistics, dating back to the early years of the Cold War, and MT is one of the biggest application areas for computational linguistics, with technical manuals, office materials, and other communications being translated daily. Unlike the translation of literary texts, where a considerable amount of creativity is required on the part of the translator, Machine Translation is focused on translations which preserve the information content of the source language as much as possible, while rendering it in a natural form in the target language. Its main advantages are economic, particularly when the volume of text is such that humans could not possibly translate it. Lower accuracy translations may be sufficient for getting the gist of some foreign language source, whereas for higher-quality results, post-editing of the machine translation by humans is often necessary. For my research in finding a new way to help those who use MT and solve these problems, I found a link between theories which maybe used to achieve better machine translation. To do so, I used Corpus Linguistics along with two other theories called, Computational Linguistics and Artificial Intelligence.
Type: Individual Paper
Status: Completed


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