Phonotactics is the study of restrictions on possible sound sequences in a language. In any language, some phonotactic constraints can be stated without reference to morphology, but many of the more nuanced phonotactic generalizations do make use of morphosyntactic and lexical information. At the most basic level, many languages mark edges of words in some phonological way. Different phonotactic constraints hold of sounds that belong to the same morpheme as opposed to sounds that are separated by a morpheme boundary. Different phonotactic constraints may apply to morphemes of different types (such as roots versus affixes). There are also correlations between phonotactic shapes and following certain morphosyntactic and phonological rules, which may correlate to syntactic category, declension class, or etymological origins.
Approaches to the interaction between phonotactics and morphology address two questions: (1) how to account for rules that are sensitive to morpheme boundaries and structure and (2) determining the status of phonotactic constraints associated with only some morphemes. Theories differ as to how much morphological information phonology is allowed to access. In some theories of phonology, any reference to the specific identities or subclasses of morphemes would exclude a rule from the domain of phonology proper. These rules are either part of the morphology or are not given the status of a rule at all. Other theories allow the phonological grammar to refer to detailed morphological and lexical information. Depending on the theory, phonotactic differences between morphemes may receive direct explanations or be seen as the residue of historical change and not something that constitutes grammatical knowledge in the speaker’s mind.
Daniel Schmidtke and Victor Kuperman
Lexical representations in an individual mind are not given to direct scrutiny. Thus, in their theorizing of mental representations, researchers must rely on observable and measurable outcomes of language processing, that is, perception, production, storage, access, and retrieval of lexical information. Morphological research pursues these questions utilizing the full arsenal of analytical tools and experimental techniques that are at the disposal of psycholinguistics. This article outlines the most popular approaches, and aims to provide, for each technique, a brief overview of its procedure in experimental practice. Additionally, the article describes the link between the processing effect(s) that the tool can elicit and the representational phenomena that it may shed light on. The article discusses methods of morphological research in the two major human linguistic faculties—production and comprehension—and provides a separate treatment of spoken, written and sign language.
Corpora are an all-important resource in linguistics, as they constitute the primary source for large-scale examples of language usage. This has been even more evident in recent years, with the increasing availability of texts in digital format leading more and more corpus linguistics toward a “big data” approach. As a consequence, the quantitative methods adopted in the field are becoming more sophisticated and various.
When it comes to morphology, corpora represent a primary source of evidence to describe morpheme usage, and in particular how often a particular morphological pattern is attested in a given language. There is hence a tight relation between corpus linguistics and the study of morphology and the lexicon. This relation, however, can be considered bi-directional. On the one hand, corpora are used as a source of evidence to develop metrics and train computational models of morphology: by means of corpus data it is possible to quantitatively characterize morphological notions such as productivity, and corpus data are fed to computational models to capture morphological phenomena at different levels of description. On the other hand, morphology has also been applied as an organization principle to corpora. Annotations of linguistic data often adopt morphological notions as guidelines. The resulting information, either obtained from human annotators or relying on automatic systems, makes corpora easier to analyze and more convenient to use in a number of applications.