It is almost impossible today to imagine the study of written academic English without the contribution of corpus linguistics. Corpora provide textual information which represent a speaker’s experience of language in a particular domain and so offer evidence of typical patterning in academic texts. It is a method which focuses on community practices and the ways members of particular disciplines see and discuss the world. This chapter describes some key studies which contribute to a corpus-informed understanding of academic writing in English. These show that a range of features have been found to occur and behave in dissimilar ways in different disciplinary and genre environments, indicating that academic discourse is a myriad of texts differing across contexts than a single entity. This will cover a state-of-the-art overview and highlight four central papers in this area, identifying their research questions, describing their methods, summarizing the major findings, and discussing their importance. The chapter then describes a representative work in greater detail, focusing on one of the author’s own studies which explores whether the considerable pressures on academics to publish and gain visibility for their work has led authors to rhetorically ‘hype’ their studies.

In 2004 Pam Peters published arguably the first usage guide to systematically use corpus methods. Peters tackled three main challenges: using corpora to find relative frequencies of prescribed and proscribed forms; presenting corpus findings clearly and concisely in a general reference book; translating that information into practical usage advice. For the first challenge, Peters mostly used corpus evidence for issues that could be evaluated with string searches, like curriculums vs. curricula, as well as a more nuanced approach to evaluating matters like the evolving meaning of reticent. For the second challenge, Peters used both numbers and relationship terms to compare frequencies of prescribed and proscribed forms. For the third challenge Peters seldom gave categorical advice or treated variation as error. Instead she presented the data and let it speak for itself; her approach emphasised understanding the factors behind variation rather than condemning it. This chapter argues that Peters is a pioneer in showing ways to use corpus data on a large scale in a general usage guide, setting a model for future usage guides and inviting further exploration of unresolved challenges.

This chapter begins with a general discussion of potential data types in variationist linguistics. Next, we present the two main data sources we use in the study: the International Corpus of English (ICE) and the Global Corpus of Web-Based English (GloWbE). The former comprises a set of parallel, balanced corpora representative of language usage across a wide range of standard national varieties. Each ICE corpus contains 500 texts of 2000 words each, sampled from twelve spoken and written genres/registers, totaling approx. 1 million words. GloWbE contains data collected from 1.8 million English language websites – both blogs and general web pages – from twenty different countries (approx. 1.8 billion words in all). Discussion of the corpora is followed by a detailed description of the data collection, identification, and annotation procedures for our three alternations. Here we carefully define the variable context for each alternation, and outline the methods for coding various linguistic constraints that are included in our analyses.

There is a correlation between the phonological shape of a word and the word’s probability in use. Less probable words tend to be longer and more probable words shorter (see Piantadosi et al. , Zipf ). This has been attributed to the lexicon evolving for efficient communication (Zipf ). To identify less probable words, listeners need more information from the segments in the phonological word itself. In this case, longer lengths for less probable words mean a greater amount of information to be used in word identification. However, this does not take into account how listeners actually process words. Research in spoken word recognition has shown that words are processed incrementally and some segments may in fact be more informative (Allopenna et al. , Luce and Pisoni , van Son and Pols , Weber and Scharenborg ). Here, we use corpus data from American English to provide evidence that less probable words contain more informative segments. We also show that the distribution of segmental information is correlated with the word’s probability and that less probable words contain more of their total information in the early segments. We discuss these findings and possible evolutionary avenues for language to reach this state. This work provides support for the idea that the words in the lexicon evolve under pressure for efficient communication.

A canonically agglutinative language or morphological pattern is traditionally analyzed as building words out of independent morphemes. Using data from Choguita Rarámuri (Uto-Aztecan), we attempt to quantify this notion by examining the extent to which meanings are predictable from their exponents without reference to context. We show that two-layer connectionist networks, computational models that map form onto meaning directly, can be used for this purpose. We also show that learning the meanings of morphemes can pose significant challenges to such models and constrains the design of the learning algorithm. In particular, models trained to equilibrium tend to focus on unreliable cues to the meanings they try to predict, especially when trained on a small corpus typical of underresourced languages. Some of these issues can be alleviated by a slow learning rate. However, one issue — which we call the problem of spurious excitement — is shown to be inherent to the learning algorithm, and always arises by the time the model achieves equilibrium. Spurious excitement means that a cue becomes associated with a meaning that it does not co-occur with, simply because of co-occurring with cues that disfavor the meaning. This case raises larger implications with respect to the type of learning mechanism involved in the acquisition of natural languages. Solutions to spurious excitement are discussed. The logistic activation function is shown to improve the performance of the model in detecting reliable cues to meanings that recur across many word types (i.e., cues of high type frequency), as well as eliminating spurious excitement.