Grammatical Features Inventory: Inflectional Class

In attempting to understand language, many researchers use features, the elements into which linguistic units, such as words, can be broken down. Examples of features are NUMBER (singular, plural, dual, ...), PERSON (1st, 2nd, 3rd), and TENSE (present, past, ...). Features have proved invaluable for analysis and description, and have a major role in contemporary linguistics, from the most abstract theorising to the most applied computational applications. Yet little is firmly established about features: we have no inventory of which features are found in the world's languages, no agreed account of how they operate across different components of language, no certainty on how they interact, and thus no general theory of features. They are used, but are little discussed and poorly understood. This is a central gap in the conceptual underpinning of much linguistic investigation. The Grammatical Features Inventory is an attempt to put the notion of linguistic 'feature' on a sounder empirical and conceptual base. It aims to provide evidence for the diverse content of features in the world's languages, as well as discuss some of their formal properties, particularly in morphology (word structure) and syntax (sentence structure). Inflectional class is the best known type of morphological feature. It partitions the vocabulary items in a given language according to the way in which they realise morphosemantic or morphosyntactic feature specifications. Corbett and Baerman (2006), who propose a typology of morphological features, argue that inflectional class features may be of two different sub-types: affixal, or prosodic. These sub-types may cross-classify the same vocabulary items - that is, the specifications for affixal and prosodic paradigms can be independent of each other. This resource was created for the project 'Grammatical features: A key to understanding language', funded by the Economic and Social Research Council under grant number RES-051-27-0122. This support is gratefully acknowledged.