Text Genres and Registers: The Computation of Linguistic Features

Introduction.- Language Resources.- Corpus Annotation and Usable Linguistic Features.- Etymological Features across Genres and Registers.- Part-of-Speech Tags and ICE Text Classification.- Verbs and Text Classification.- Adjectives and Text Categories.- Adverbial Clauses across Text Categories and Registers.- Coordination across Modes, Genres and Registers.- Semantic Features and Authorship Attribution.- Pragmatics and Dialogue Acts.- The Future.- Bibliography.- Appendix.- Index.

Alex Fang is based at the Department of Chinese, Translation and Linguistics, City University of Hong Kong and lectures on topics devoted to corpus linguistics, computational linguistics and machine translation. He is also Adjunct Professor at the Beijing University of Aeronautics and Astronautics in Beijing, China. He is Director of the Dialogue Systems Group (http://dsg.ctl.cityu.edu.hk) and currently supervises 6 PhD students. He has published widely and his most recent monographs include English Corpora and Automated Grammatical Analysis (2007) and Contributions of Syntax to Terminology Extraction (2010). He is National Expert representing China in the International Organisation for Standardisation (ISO) on Technical Committee 37 for terminology and language resources, where he has participated in the drafting of several international standards for language resource annotation. He is also an appointed expert member of the China National Technical Committee for the Standardization of Terminologies and Language Resources. He serves on the programme committee of several major international conferences on computational linguistics. He was previously Deputy Director of the Survey of English Usage, University College London, where he received his PhD in linguistics.

"This book would be a useful addition to the field of corpus-based computational analysis by tactically connecting corpus perspective and NLP perspective. ... In summation, their methodology of research represented by these empirical studies, including their choices of corpora, annotation schemes, and evaluation measures, could set very good examples for future researchers, especially for those who are familiar with both corpus linguistics and NLP." (Fan Pan and Guoxiao Tao, Scientometrics, Vol. 113, 2017)