Signalling Nouns in English A Corpus-Based Discourse Approach [Paperback]

The first book-length treatment of signalling nouns in academic English that combines discourse and corpus-based approaches.This study explores 'signalling nouns' - nouns whose meaning can only be determined by the context in which they are used. It investigates how they function in academic discourse, what their semantic properties are, and the linguistic environments in which they can and cannot occur.This study explores 'signalling nouns' - nouns whose meaning can only be determined by the context in which they are used. It investigates how they function in academic discourse, what their semantic properties are, and the linguistic environments in which they can and cannot occur.Signalling nouns (SNs) are abstract nouns like 'fact', 'idea', 'problem' and 'result', which are non-specific in their meaning when considered in isolation and specific in their meaning by reference to their linguistic context. SNs contribute to cohesion and evaluation in discourse. This work offers the first book-length study of the SN phenomenon to treat the functional and discourse features of the category as primary. Using a balanced corpus of authentic data, the book explores the lexicogrammatical and discourse features of SNs in academic journal articles, textbooks, and lectures across a range of disciplines in the natural and social sciences. The book will be essential reading for researchers and advanced students of semantics, syntax, corpus linguistics and discourse analysis, in addition to scholars and teachers in the field of English for academic purposes.1. Introduction; 2. Grammatical features of signalling nouns; 3. Semantic features; 4. Discourse features; 5. Criteria for determining what constitutes a signalling noun in this study; 6. Corpus, methodology, annotation system, and reporting of the data; 7. Set of examples; 8. Overview of signalling noun distributions in the corpus; 9. Overview of semantic categories; 10. Overview of lexicogrammatical and discourse pattern freqlS%