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.