This book provides a corpus-led analysis of multi-word units (MWUs) in English, specifically fixed pairs of nouns which are linked by a conjunction, such as 'mum and dad', 'bride and groom' and 'law and order'. Crucially, the occurrence pattern of such pairs is dependent on genre, and this book aims to document the structural distribution of some key Linked Noun Groups (LNGs). The author looks at the usage patterns found in a range of poetry and fiction dating from the 17th to 20th century, and also highlights the important role such binomials play in academic English, while acknowledging that they are far less common in casual spoken English. His findings will be highly relevant to students and scholars working in language teaching, stylistics, and language technology (including AI).

The highly frequent word items TO and OF are often conceived merely as prepositions, carrying little meaning in themselves. This book disputes that notion by analysing the usage patterns found for OF and TO in different sets of text corpora.

This book explores the interconnections between linguistics and Artificial Intelligence (AI) research, their mutually influential theories and developments, and the areas where these two groups can still learn from each other. It begins with a brief history of artificial intelligence theories focusing on figures including Alan Turing and M. Ross Quillian and the key concepts of priming, spread-activation and the semantic web. The author details the origins of the theory of lexical priming in early AI research and how it can be used to explain structures of language that corpus linguists have uncovered. He explores how the idea of mirroring the mind's language processing has been adopted to create machines that can be taught to listen and understand human speech in a way that goes beyond a fixed set of commands. In doing so, he reveals how the latest research into the semantic web and Natural Language Processing has developed from its early roots. The book moves on to describe how the technology has evolved with the adoption of inference concepts, probabilistic grammar models, and deep neural networks in order to fine-tune the latest language-processing and translation tools. This engaging book offers thought-provoking insights to corpus linguists, computational linguists and those working in AI and NLP.

This book shows that over forty years of psychological laboratory-based research support the claims of the Lexical Priming Theory. It examines how Lexical Priming applies to the use of spoken English as the book provides evidence that Lexical Priming is found in everyday spoken conversations.

Corpus Linguistics is becoming an increasingly important branch of language research and interest has spread noticeably beyond the confines of academia, fuelled by applications like text predicting software. The idea of priming in language goes back to the early 1960s with the concept of a 'Teachable Language Comprehender', which started experiments into language processing and which inspired one of Google's chief engineers. The concept of Lexical Priming (Hoey: 2005) aims to supply answers as to how we can explain word choices and construction forms that are more frequent than laws of probability would allow. This book provides a range of arguments to support the validity of Lexical Priming as a linguistic theory, while it also extends the reach of what Lexical Priming has been used to describe. Beyond the written-text material originally used, this book provides evidence that lexical priming also applies to everyday spoken conversations as its psychological foundations predict that it should.