Informing Lexicographic Choices Through Corpus and Perceptual Data

Abstract

Abstract Lexicographers make many choices when compiling dictionary entries. Data-driven approaches can better inform such decisions. This paper proposes integration of two such sources, corpus data and perceptual survey data. A survey was distributed to 100 native speakers of English. Participants were asked to provide a definition and example sentence for 18 polysemous English words. After providing definitions, participants were presented with five definitions and asked to rank them by importance. Patterns in the rankings were analyzed to see what factors predicted higher ratings. Frequency was found to predict rankings, while concreteness was also shown to be moderate a predictor and have a potentially strong effect on certain words. Part of speech was not found to be a significant predictor of rank. Results suggest that crowdsourcing may be useful for certain linguistic tasks and that both corpus and perceptual data can be useful in making lexicographic choices.