This chapter introduces nouns in Chinese and provides specific characteristics, including meaning and use. Noun affixes and their usages are also introduced. These are supplemented by discussions on the functions of nouns in a sentence.

This chapter aims to provide a careful examination of Mandarin Chinese classifiers from a syntactic perspective. A comprehensive overview of the distribution of classifiers is provided along with their syntactic analyses. A central conclusion of this chapter, following much recent work, is that there are two distinct structural configurations that numeral classifiers participate in, and that these structures can distinguish the type of classifier participating in the structure as well as its semantic interpretation. The syntactic analyses are complemented with formal semantic analyses of numeral classifiers.

The skeletal structure of a sentence is defined by the propositional acts of reference, predication, and modification. Reference is carried out by a referring phrase. The prototypical head of a referring phrase denotes an object; this is a noun. Modifiers are dependents of a noun that form attributive phrases. The prototypical head of an attributive phrase denotes a property; this is an adjective. A clause predicates something of a referent or referents. The prototypical head of a clause denotes an action; this is a verb. Reference, modification, and predication of nonprototypical concepts is possible, and often expressed by distinct constructions. Three principles govern how combinations of information packaging and semantic content are expressed: any concept can be packaged in any way; some ways are more ‘natural’ than others; and how they are packaged is constrained by conventions of the speech community. Nonprototypical constructions often share properties of ‘neighboring’ prototypical constructions. They often differ by having additional forms coding the nonprototypical function, and/or by a lesser potential for expressing associated grammatical categories (e.g., inflections).

Nouns head nominals, which head noun phrases (NPs). The most common NP functions are subject, object, and predicative complement. Nouns mostly inflect for number: singular or plural. Pronouns are a special subset of nouns which also inflect for case. A nominal includes a head noun and any internal dependents. Unlike most phrases, nominals can have adjective-phrase modifiers, and NPs uniquely may have a constituent in determiner function.

Though it’s true that only nouns denote ‘people, places, and things’, they denote almost anything, including actions. Along with number, the semantic notion of definiteness and the count/non-count distinction affect the choice of determiner. Subject-verb agreement is also affected, for example, with measure expressions. Determiners are usually determinative phrases or genitive NPs. Nominals allow complements, usually preposition phrases. NPs also have a range of external dependents, including predeterminer modifiers. Determiners and modifiers may function as fused heads, in which case, the NP may not actually include a noun. The pronouns, including personal, relative, and interrogative types, have deictic and anaphoric uses and notably have gender.

This chapter looks at nine nouns in Heaney poems, and considers how each shoulders the weight of narrative or meaning in its poem. The chosen nouns are scoop, bucket, lorries (two), car, bicycle, rope and two individual doors. The chapter examines Heaney’s own exhortation in ‘Oysters’, ‘Verb, pure verb’, putting the physical objects and their too-solid nouns at the centre of a close reading of eight poems, including ‘Sunlight’, ‘A Constable Calls’, ‘Súgán’ and ‘Postscript’. The chapter argues that nouns provide a kind of touchstone reality in the poems and are emblems and artefacts of the idea of belonging, deployed to service the relationship between the physical (often domestic) world and the human experience of it.

There is an ongoing debate about the analysis of argument structure in a usage-based construction grammar. Some scholars have argued that argument structure is licensed by fully abstract schemas, but other researchers have claimed that argument structure is primarily determined by particular verbs. Chapter 7 argues that this controversy is easily resolved if we analyze argument structure in the framework of a network model in which verbs and constructions are interconnected by probabilistic links. For instance, the two constructions of the dative alternation occur with an overlapping set of verbs that are statistically biased to be used in one or the other constructions. The statistical biases can be analyzed as filler-slot associations that are shaped by two factors: (1) general conceptual processes of event semantics and (2) speakers’ experience with particular verbs and constructions. The analysis is supported by evidence from research on sentence processing and the extension of argument schemas to novel verbs in L1 acquisition and language change.

Chapter 8 extends the network analysis of argument structure to the analysis of parts of speech. Traditionally, parts of speech are analyzed as classes of lexical items with the same or similar structural properties, but the structural criteria that are used to define the major parts of speech (e.g., the occurrence of certain function words or inflectional affixes) can also be seen as properties of particular slots of constructional schemas. Crucially, while the slots of word class schemas are commonly defined by distributional criteria, they are not merely structural concepts but evoke particular conceptualizations. Combining research from cognitive linguistics with research from typology, the chapter argues that the major parts of speech are best analyzed in the framework of a network model in which particular lexical items are linked to particular word class schemas. The bulk of the analysis is concerned with the three major parts of speech (i.e., nouns, verbs, adjectives), but the chapter also includes a section on grammaticalization that explains how grammatical function words are derived from content words (and demonstratives) in a dynamic network model.