Richard Xiao

This title explores the theoretical and practical issues pertaining to the creation and use of corpora in contrastive and translation studies. It represents the latest developments in corpus-based translation studies, corpus-based contrastive studies, parrallel corpus development and bilingual lexicography.

Using Corpora in Contrastive and Translation Studies

Tony McEnery

This book is concerned with cross-linguistic contrast of major grammatical categories in English and Chinese, two most important yet genetically different world languages. This genetic difference has resulted in many subsidiary differences that are, among other things, related to grammar. Compared with typologically related languages, cross-linguistic contrast of English and Chinese is more challenging yet promising. The main theme of this book lies in its focus on cross-linguistic contrast of aspect-related grammatical categories, or, grammatical categories that contribute to aspectual meaning – both situation aspect at the semantic level and viewpoint aspect at the grammatical level – in English and Chinese. The unique strength of this volume lies in that it is first corpus-based book contrasting English and Chinese. Given that the state of the art in language studies is to use corpora, the significance of the marriage between contrastive studies and the corpus methodology in this book is not to be underestimated.

Corpus-Based Contrastive Studies of English and Chinese

Chinese, as an aspect language, has played an important role in the development of aspect theory. This book is a systematic and structured exploration of the linguistic devices that Mandarin Chinese employs to express aspectual meanings. The work presented here is the first corpus-based account of aspect in Chinese, encompassing both situation aspect and viewpoint aspect. In using corpus data, the book seeks to achieve a marriage between theory-driven and corpus-based approaches to linguistics. The corpus-based model presented explores aspect at both the semantic and grammatical levels. At the semantic level a two-level model of situation aspect is proposed, which covers both the lexical and sentential levels, thus giving a better account of the compositional nature of situation aspect. At the grammatical level four perfective and four imperfective aspects in Chinese are explored in detail. This exploration corrects many intuition-based misconceptions, and associated misleading conclusions, about aspect in Chinese common in the literature.

A Corpus-based Study

Aspect in Mandarin Chinese

Paul Rayson

A Frequency Dictionary of Mandarin Chinese is an invaluable tool for all learners of Mandarin Chinese, providing a list of the 5,000 words and the 2,000 Chinese characters (simplified) most commonly used in the language. Based on a fifty-million-word corpus composed of spoken, fiction, non-fiction and news texts in current use, the dictionary provides the user with a detailed frequency-based list, as well as alphabetical and part-of-speech indexes. All entries in the frequency list feature the English equivalent and a sample sentence with English translation. The Dictionary also contains thirty thematically organized lists of frequently used words on a variety of topics such as food, weather, travel and time expressions. A Frequency Dictionary of Mandarin Chinese enables students of all levels to maximize their study of Mandarin vocabulary in an efficient and engaging way. It is also an excellent resource for teachers of the language. A CD version is available to purchase separately. Designed for use by corpus and computational linguists it provides the full text in a format that researchers can process and turn into suitable lists for their own research work.

Core Vocabulary for Learners

A Frequency Dictionary of Mandarin Chinese

Yukio Tono

Anthony McEnery

Covering the major approaches to the use of corpus data, this work gathers together influential readings from leading names in the discipline, including Biber, Widdowson, Sinclair, Carter and McCarthy.

Richard Xiao

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