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This book is the first volume of a two-volume set that synchronically and diachronically studies the Jin dialect in Northern Shaanxi Province in China, with a focus on the grammatical features of the pronouns, the aspect and appearance, and the tense system of the dialect.
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This concise volume offers an accessible introduction to state-of-the-art AI language models, providing a platform for their use in textual interpretation across the humanities and social sciences.
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Corpus Linguistics for Virtual Workplace Discourse provides a thorough and practical step-by-step guide to constructing and analysing a multi-modal... Læs mere
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This Element introduces a usage-based computational approach to Construction Grammar that draws on techniques from natural language processing... Læs mere
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Corpus Linguistics for Oral History takes a step-by-step approach to presenting how corpus linguistics tools and techniques can be applied to oral history archives.
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This book is a convergence of heterogeneous insights (from languages and literature, history, music, media and communications, computer science and information studies) which previously went their separate ways;
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This book is the first linguistic study that combines CL and CDA to compare the media representations of Macau’s gaming industry in English-language newspapers published in Mainland China, Hong Kong, and Macau.
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This book constitutes the refereed proceedings of the 24th Workshop on Chinese Lexical Semantics, CLSW 2023, held in Singapore, during May 19–21, 2023. The 61 full papers and 15 short papers included in this book were carefully reviewed and selected from 215 submissions.
Bemærk: Kan ikke leveres før jul.
This book constitutes the refereed proceedings of the 24th Workshop on Chinese Lexical Semantics, CLSW 2023, held in Singapore, during May 19–21, 2023. The 61 full papers and 15 short papers included in this book were carefully reviewed and selected from 215 submissions.
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(2) adds four new chapters to introduce pre-trained language models, robust representation learning, legal knowledge representation learning and biomedical knowledge representation learning;