Formularity, or the poet’s reliance on prefabricated linguistic features in the composition of his verses, has been the most debated feature of Oral-Formulaic Theory. This chapter reviews the history of Homeric formularity (Part 1), while introducing new key insights from the fields of linguistics (esp. usage-based linguistics, corpus linguistics, and language acquisition studies) and the cognitive sciences (Parts 2-5). Parts 2-3 argue that formularity is a general feature of human language and cognition. Homer’s formularity is quantitatively notable, however, in that it involves sequences that are particularly long when compared to repeated sequences in corpora of both contemporary written or spoken English and ancient prose and hexameter authors. This is interpreted as a sign of Homer’s extreme mastery of his medium, which was arguably necessitated by the oral-improvisational nature of the task. Part 4 develops a new theory of Homeric formularity, borrowing insights from connectionism, lexical priming, and construction grammar, and introduces fine-grained distinctions between conceptual associations, collocations, constructions, metrical constructions and structural formulas.

Collocation methods for elliptic problems are discussed here. We begin by providing their definition. For their analysis we first introduce a weighted weak formulation of the problem, and show that it is well posed. Then, we introduce and analyze a Galerkin approximation for this problem, where the subspace consists of polynomials that vanish sufficiently fast at the boundary. Next, a scheme with quadrature is proposed, and its analysis is provided using the theory of variational crimes and Strang lemmas. For its implementation and analysis the discrete cosine and Chebyshev transforms are introduced and analyzed. The phenomenon of aliasing is briefly discussed. Finally, we connect the weighted Galerkin approximation with quadrature to collocation methods, thus providing an analysis of collocation schemes.

There is now plenty of evidence that the learning of multiword units can occur across the four strands of meaning-focused input, meaning-focused output, language-focused learning, and fluency development, although the largely unresearched strand with regard to multiword units is fluency development. There are now many useful well-researched lists of multiword units of various kinds. Multiword units tend to be acquired late and are one of the signs of high proficiency in a language. This chapter looks at various ways of classifying multiword units according to their form, meaning, and storage. It has many practical suggestions for supporting the learning of multiword units. These include learning through input, learning through output, consciousness raising, using flash cards, using mnemonic tricks such as alliteration and considering their origins, looking for patterns, using concordances, and fluency development.

Words are not isolated units of the language but fit into many related systems. Because of this, there are many things to know about any particular word and there are many degrees of knowing. The aims of this chapter are to examine what could be known about a word, to evaluate the relative importance of the various kinds of knowledge, to see how they are related to each other, and to broadly suggest how learners might gain this knowledge. The chapter also looks at the learning burden of words, that is, what needs to be learned for each word and what is predictable from previous knowledge. The chapter is based on the division of what it means to know a word into nine aspects of knowledge – spoken form, written form, word parts, form–meaning connection, concepts, associations, grammatical functions, collocations, and constraints on use. The chapter concludes with the description of a model of vocabulary learning.

In Chapter 6, I argue that the terms ‘mental health’ and ‘mental illness’ have been used interchangeably in the previous literature on the representation of mental illness in the press. Specifically, I argue that using these two terms interchangeably (especially during data collection) may result in incomparable datasets. Through linguistic analysis, I show that the terms ‘mental illness’ and ‘mental health’ are distinct terms, and that the meaning of the two terms has shifted over the time period covered by the MI 1984–2014 Corpus. I argue that the lexical change I observed is consistent with pragmatic accounts of language change in which the language development is in part a result of euphemism (e.g. Traugott & Dasher, 2002).

Chapter 8 explores the ways in which the press talk about people having mental illness using a mixed-methods approach. In the chapter, the frequency and semantic and pragmatic content of the verbs ‘suffer’ and ‘ experience’ in the context of prescribed forms for talking about having mental illness are investigated. I show that ‘suffer’ and ‘experience’ occur in different semantic contexts in the MI 1984–2014 Corpus as well as general language corpora, which may contribute to ‘suffer’ being a more problematic term for describing mental health than ‘experience’. Moreover, I show that ‘suffer’ is proportionally less likely to be used in first-person narratives because ‘suffering’ is attributed to people with mental illness by others, for example, medical professionals, in reported speech. I bring together my findings in a set of lexicogrammatical heuristics based on the semantic content of ‘suffer’ and ‘experience’ in context (e.g. whether the word encodes animacy or is temporally bounded).

Chapter 3 provides a brief overview of the particular approach to corpus linguistics adopted in this book: namely, corpus linguistics as a method (as opposed to corpus linguistics as a theory) (McEnery & Hardy, 2012; Tognini-Bonelli, 2001). The specific corpus linguistic analytical methods used, such as collocation and keyness analysis, and the statistical tests and cut-offs associated with each analysis type are detailed. Using data from the MI 1984–2014 Corpus (specifically the data collected during a pilot study and an illness-specific sample of the data), each analytical method used is exemplified. The utility of each analysis type for analysing ideology in texts is also discussed.

This is a ‘quantitative methods’ chapter. It describes the basic quantitative concepts in corpus linguistics. These are: frequency and normalised frequency; range and dispersion; the concept of keyness; collocation; and lexical bundles. In each case research using these ideas is discussed and issues around the different available measures are presented. The chapter then introduces multidimensional analysis and how quantitative studies might be enhanced by semantic and other annotation. The emphasis in the chapter is on how the various measures are used and the impact they have on the applications of corpus research.

Chapter 5 describes the fundamental research questions, empirical approaches and findings of corpus linguistics. Basically, it is an empirical approach investigating language use in its natural context with different types of corpora as its data base. Methodological issues include considerations on corpus linguistic approaches, types and criteria of corpora, steps of corpus analysis, such as tokenisation and tagging, and finally types of analysis. The chapter ends with recommendations for further reading and a list of short exercises and ideas for small research projects.

This chapter traces the development of monolingual learners’ dictionaries (MLD) from their genesis in the 1930s through their current internet editions. Starting from the pioneering work of West, Palmer, and Hornby, it shows how the aim of enabling learners to read and write English effectively informed the developing content of MLDs, from the Oxford Advanced Learner’s through the Longman, Collins, Cambridge, Macmillan, and American Merriam-Webster dictionaries. The introduction of explicit information on grammatical and lexical patterning including collocations and idioms, the use of a limited defining vocabulary, the use of a computer corpus of texts, and the inclusion of frequency information all contributed to the profile of the MLD as it is known today. Increasing concern for accessibility has influenced both the layout of dictionary entries and the presentation of word senses in longer entries, with the use of guide words and menus. The chapter ends with a brief review of the benefits and challenges of migrating MLDs to the electronic medium, especially the Internet.

Chapter 6 analyzes word association responses, categorizes them into meaning-based and syntagmatic and compares to the patterns of corresponding usage corpora. It shows that words eliciting meaning-based responses tend to be independent in usage while words eliciting syntagmatic responses tend to participate in multi-word units, suggesting that word associations can indeed say something about the processes at work in language use. A deeper analysis of syntagmatic associations and their comparison to usage patterns suggest the psycholinguistic reality of the model of a unit of meaning and in particular of abstracted associations: those of colligation and semantic preference. The chapter also discusses the core meaning effect, the influence of directionality and contiguity on the strength of association, the relationship of syntagmatic association to the boundaries of a unit of meaning as well as the evidence of the processes of fixing and approximation observed in Chapter 5.

Chapter 5 compares the phraseology of usage to exposure. It shows that more than half of patterns extracted from a student’s usage corpus also occur in her exposure corpus. At the same time the figure drops significantly if these patterns are compared to a different student’s exposure corpus supporting the assumption of representativeness. The chapter then proceeds to compare usage patterns to exposure qualitatively focusing on the processes of variation and change. It finds support for the process of approximation through which a more or less fixed pattern loosens and becomes variable on the semantic or grammatical axis presumably due to frequency effects and the properties of human memory. The chapter also proposes a reverse process, fixing, through which the pattern extends and develops verbatim associations through repeated usage. Both processes are suggested to occur within meaning-shifts units and thus be characteristic of co-selection.

Chapter 2 provides an in-depth discussion of Sinclair’s conceptualization of lexis and meaning and its major concepts. It starts from the model of a unit of meaning and explains how it is capable of incorporating both syntagmatic and paradigmatic axes of meaning by including optional variable components of collocation, colligation and semantic preference. The chapter continues by offering a theoretical solution of removing the borderline between single- and multi-word units. Further it points out the difference between Firth’s and Sinclair’s conceptions of collocation and defines the relationships between the idiom principle, co-selection, syntagmatic association, core meaning, delexicalization and meaning-shift. A large part of the chapter is devoted to examining the controversy around the concept of semantic prosody. The chapter concludes by discussing Sinclair’s theory of meaning and his idea of the ultimate dictionary. The conceptualization presented in the chapter forms the theoretical backbone of the book.

All aspects of linguistic knowledge are ultimately based on speakers’ experience with lexical expressions, but of course, knowledge of language, notably, grammar, exceeds their memory of particular lexical tokens. It is a standard assumption of the usage-based approach that grammar involves a taxonomic network of constructions ranging from prefabricated strings of lexical expressions to highly abstract schemas. Chapter 4 describes the taxonomic organization of constructions and their development in L1 acquisition and language change. It includes a detailed discussion of current research on statistical grammar learning in infancy, the acquisition of constructions during the preschool years and two case studies on the rise of constructional schemas in language history.

All linguistic elements, e.g., words, phrases and clauses, occur in sequential order. The sequential arrangement of linguistic elements is motivated by conceptual and pragmatic factors, but the strength of sequential relations is primarily determined by automatization or chunking. Since automatization is a gradual process driven by frequency of occurrence, sequential relations vary on a continuum. Moreover, since language unfolds in time, sequential relations have an inherent forward orientation, which is reflected in the fact that listeners are able to “predict” upcoming elements in the unfolding speech stream. Chapter 5 considers the effect of automatization and chunking on the development of linguistic structure and the cognitive organization of grammar. The chapter is divided into two parts. The first part is concerned with research on lexical prefabs and the organization of morphological network models, and the second part considers sequential aspects of constructional schemas and the gradience of constituency.

Translation is a demanding, competitive, and labour-intensive activity that requires more than just advanced language skills. If translators are to thrive in such a cutting-edge working environment, they need to possess good computer literacy skills and be well-versed in the art of strategic information mining - in other words, they need to know how to make best use of the plethora of dictionaries, glossaries, books, websites, and other online resources at their disposal in order to find the required information in the shortest amount of time possible. This includes finding translation equivalents and domain-specific terms, checking collocations, idioms, and phrasal verbs, and exploring the way words are used in context by native speakers in order to render the text grammatically, semantically, and stylistically appropriate. Corpora, i.e. large electronic collections of computer-processed, linguistically annotated texts, can serve as an invaluable source of linguistic information, providing the translator with a sample of genuine texts in the target language and, in some cases, translations of similar texts. Over the past two decades, a wide variety of monolingual, comparable, and parallel corpora have been compiled, and these can be searched using either downloadable or online corpus query systems (CQS) such as the Sketch Engine. Whilst computer-assisted translation (CAT) tools such as SDL Trados and MemoQ are a staple of the translation process, translators are still relatively conservative when it comes to using corpora and CQSs to inform their practice. This chapter will introduce the Sketch Engine as a powerful suite of corpus tools for translation and cross-linguistic analysis. Centred around a case study featuring a real-life translation scenario, it will discuss various features of the software which can be used to leverage very large web-based monolingual and parallel corpora for translation, i.e. the concordancer, the word sketches tool, the thesaurus, the term extraction feature, and the corpus building tool.