How do children process language as they get older? Is there continuity in the functions assigned to specific structures? And what changes in their processing and their representations as they acquire more language? They appear to use bracketing (finding boundaries), reference (linking to meanings), and clustering (grouping units that belong together) as they analyze the speech stream and extract recurring units, word classes, and larger constructions. Comprehension precedes production. This allows children to monitor and repair production that doesn’t match the adult forms they have represented in memory. Children also track the frequency of types and tokens; they use types in setting up paradigms and identifying regular versus irregular forms. Amount of experience with language, (the diversity of settings) plus feedback and practice, also accounts for individual differences in the paths followed during acquisition. Ultimately, models of the process of acquisition need to incorporate all this to account for how acquisition takes place.

This chapter provides an inventory of maximizer types and tokens attested in the data. Altogether 23 maximizers, covering both full and zero forms, were included in the study, totalling 9,488 relevant tokens; the four top-frequency items comprise perfectly, too, most and entirely. The diachronic distribution of the top seven maximizers across the period studied is discussed, with comparisons made between usage in the Late Modern English and the modern BNC trials data. The maximizers prove to be the only category of intensifiers increasing across the period studied; boosters and downtoners show declining rates of use. The semantic input domains of the maximizers are discussed, and the targets of intensification and the collocational features in usage patterns presented. Maximizers mainly modify adjectives and less so adverbs and verbs. Within the category of maximized adjectives, the category of Human Propensity dominates; within the maximized category of verbs, the material process types cover most of the uses. Finally, the collocates and semantic prosodies of the top seven maximizers are described, with attention paid to the situation-specific and relatively fixed uses.

The boosters found in the Old Bailey Corpus (1720–1913) are documented in this chapter, with regard to their overall frequency distributions and usage patterns. This includes an overview of the entire inventory of 44 types and 47,613 tokens, which makes it the largest intensifier group in the data. Very is found to dominate the data, followed by far less frequent so and greatly as well as many fairly low-frequency items. Semantically, boosters are subdivided into originally quantitative (denoting amount: greatly, extent: widely) and qualitative types (e.g., denoting truth: very, perception: strikingly or evaluation: badly). Formally, the two most frequent types are unmarked adverbs (very, so); two other boosters prefer the suffixless form to a large extent (great, wide). The targets modified by boosters are mostly adjectives, followed by adverbs, while verbs and prepositional phrases are rare.

This chapter is devoted to downtoners, namely moderators, diminishers and minimizers, with the 19 attested types amounting to 7,874 examples. The dominant type a little constitutes 66 per cent of the occurrences and is followed by hardly with 13 per cent. The distribution of the five most frequent downtoners across the period studied is discussed, and compared to the BNC trials data. The decline in the use of diminisher a little accounts for the overall decline in the use of downtoners in the OBC data. The source terms of downtoners display a more varied spectrum of semantic shades than maximizers and boosters. There is also a greater variety of target categories than attested for boosters and maximizers: the otherwise most frequent targets adjectives are here outranked by prepositional phrases and verbs, with the latter standing out as the specialty of downtoners compared to all other intensifiers. They predominantly modify verbs of the material and mental process types; in the semantic classes of downtoned adjectives, the category of human propensity dominates. As for collocational profiles, for instance a little dominates in collocations with after, before and more.

This chapter looks at what vocabulary and how much vocabulary needs to be learned. It is useful to use frequency and range of occurrence to distinguish several levels of vocabulary. Distinguishing these levels helps ensure that learners learn vocabulary in the most useful sequence and thus gain the most benefit from the vocabulary they learn. Making the high-frequency/mid-frequency/low-frequency distinctions ensures that the teacher and learners deal with vocabulary in the most efficient ways. High-frequency words are the most useful words of the language and should be learned first. There are 3,000 high-frequency words. These should be followed by mid-frequency words or specialised vocabulary. The mid-frequency and low-frequency words should not be taught but should be learned through extensive listening and extensive reading, along with the use of vocabulary learning strategies such as flash cards, word part analysis. and dictionary use.

We define a linguistic distribution as the range of values for a quantitative linguistic variable across the texts in a corpus. An accurate parameter estimate means that the measures based on the corpus are close to the actual values of a parameter in the domain. Precision refers to whether or not the corpus is large enough to reliably capture the distribution of a particular linguistic feature. Distribution considerations relate to the question of how many texts are needed. The answer will vary depending on the nature of the linguistic variable of interest. Linguistic variables can be categorized broadly as linguistic tokens (rates of occurrence for a feature) and linguistic types (the number of different items that occur). The distribution considerations for linguistic tokens and linguistic types are fundamentally different. Corpora can be “undersampled” or “oversampled” – neither of which is desirable. Statistical measures can be used to evaluate corpus size relative to research goals – one set of measures enables researchers to determine the required sample size for a new corpus, while another provides a means to determine precision for an existing corpus. The adage “bigger is better” aptly captures our best recommendation for studies of words and other linguistic types.

Composition, types, mechanism of action, efficacy, effectiveness, health risks, benefits, usage and follow up of non-hormonal preparations in utero are discussed

Objective: Delve into programming logic and flow, VBA syntax, and debugging tools. Become familiar with code structure, communication with spreadsheets, dynamic data storage, conditional statements and loops, calling worksheet functions, and creating user-defined ones.

In this chapter we introduce the C(M) language, a new programming language. C(M) statements and expressions closely resemble the notation commonly used for the presentation of formal constructions in a Tarskian style set theoretical language. The usual set theoretic objects such as sets, functions, relations, tuples etc. are naturally integrated in the language. In contrast to imperative languages such as C or Java, C(M) is a functional declarative programming language. C(M) has many similarities with Haskell but makes use of the standard mathematical notation like SETL. The C(M) compiler translates a well-formed C(M) program into efficient C code, which can be executed after compilation. Since it is easy to read C(M) programs, a pseudo-code description becomes obsolete.