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Abstract Entity Anaphora in Argumentative Texts
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Abstract Entity Anaphora in Argumentative Texts : Pragmatic Features and Referent Interpretation

2023 ed.

Book Details

Format Paperback / Softback
ISBN-10 9819946328
ISBN-13 9789819946327
Edition 2023 ed.
Publisher Springer Verlag, Singapore
Imprint Springer Verlag, Singapore
Country of Manufacture GB
Country of Publication GB
Publication Date Aug 23rd, 2024
Print length 167 Pages
Ksh 18,000.00
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This book focuses on abstract entity anaphora in argumentative texts with Asher’s (1993) Segmented Discourse Representation Theory (SDRT) as the theoretical framework, investigating its pragmatic features and exploring its referent interpretation.
This book focuses on abstract entity anaphora in argumentative texts with Asher’s (1993) Segmented Discourse Representation Theory (SDRT) as the theoretical framework, investigating its pragmatic features and exploring its referent interpretation. The data sources include more than 160,000-word argumentative texts (80,000-word English texts and 80,000-word Chinese ones) selected from newspapers, journals, and books in China and America. At first, a comparative study was done between Chinese and English argumentative texts so as to compare the pragmatic features of abstract entity anaphora in the two languages. Then, referent interpretation is explored within the SDRT framework. Although SDRT can account for most of the instances of abstract entity anaphora, it appears incompetent in dealing with some phenomena in the data of our study. Seven problems in SDRT were found, and corresponding solutions were proposed in an attempt to improve this theory. In general, this book hasthree aspects of significance. Firstly, it establishes abstract entity anaphora as an independent and a special kind of anaphora. Secondly, the research methods are the combination of empirical study and theoretical hypotheses as well as the coalescent of dynamic study and static study. Thirdly, the book is not limited to the application of SDRT to Mandarin Chinese and backward anaphora. Instead, based on the linguistic phenomena in the data, it challenges and improves the theory, and it even negates some aspects and meanwhile brings forward new solutions.

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