Empirical studies and formal descriptions of verb features and verb senses:
these are some of the key fundamental factors in verb treatment, and are relevant for representing and distinguishing verbs across disciplines.
Representation of verbs by verb classes:
generalisation is crucial to the acquisition of verbs and categorisation in cognitive linguistics, and for many computational linguistic tasks; computational learning of verb classes and properties provides insights into argument alternations, verb polysemy, selectional preferences, etc.
Cognitively motivated models of verbs:
the definition of verb semantics according to human perception, the collection of human judgements on verb senses and verb properties, and psycholinguistic studies and experiments on verbs are important interdisciplinary contributions to verb characterisation.
Evidence from cognitive neuroscience and neuropsychology on verb features. Corpus-based methods to extract empirical features:
the distributional account of verb senses and verb features provides essential contributions to verb analysis. We also welcome contributions on the use of distributional data to model (neuro)cognitive evidence on verb representation.
Data resources and tools:
the definition of verb senses and verb properties are important for basic and task-oriented research; especially the annotation of lexical verb information provides valuable data to computational learning procedures and evaluation methods.
Language-specific and cross-linguistic aspects of verbs:
which verb features are specific to a language, and which are universal?