Fr. 55.50

Signalling Nouns in English - A Corpus-Based Discourse Approach

English · Paperback / Softback

Shipping usually within 3 to 5 weeks

Description

Read more










The first book-length treatment of signalling nouns in academic English that combines discourse and corpus-based approaches.

List of contents










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 frequencies; 11. Conclusion; References; Appendix A. The overall structure of the corpus; Appendix B. List of texts that make up the corpus; Appendix C. Lemmatised SNs in descending order according to normalised frequency; Appendix D. Non-lemmatised SNs in descending order according to normalised frequency; Appendix E. Lemmatised SNs in alphabetical order; Appendix F. Non-lemmatised SNs in alphabetical order; Appendix G. Frequency of SNs in different semantic categories.

About the author

John Flowerdew is a Professor in the Department of English at City University of Hong Kong.Richard W. Forest is an Assistant Professor in the Department of English Language and Literature at Central Michigan University.

Summary

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.

Customer reviews

No reviews have been written for this item yet. Write the first review and be helpful to other users when they decide on a purchase.

Write a review

Thumbs up or thumbs down? Write your own review.

For messages to CeDe.ch please use the contact form.

The input fields marked * are obligatory

By submitting this form you agree to our data privacy statement.