VROAV: Using iconicity to visually represent abstract verbs

ness is a feature of semantics that limits our ability to visualise every conceivable concept represented by a word. By tapping into the visual representation of words, we explore the common semantic elements that link words to each other. Visual languages like sign languages have been found to reveal enlightening patterns across signs of similar meanings, pointing towards the possibility of identifying clusters of iconic meanings in words. Thanks to this insight, along with an understanding of verb predicates achieved from VerbNet, this study produced VROAV (Visual Representation of Abstract Verbs): a novel verb classification system based on the shape and movement of verbs. The outcome includes 20 classes of abstract verbs and their visual representations, which were tested for validity in an online survey. Considerable agreement between participants, who judged graphic animations based on representativeness, suggests a positive way forward for this proposal, which may be developed as a language learning aid in educational contexts or as a multimodal language comprehension tool for digital text.