Abstract

Traditional dictionaries aim at including neologisms and new word senses, and are regularly updated, but they require enough textual evidence for inclusion and are often slow at reacting to linguistic innovation. On the other hand, online crowd-sourced dictionaries like Urban Dictionary (UD) and Wiktionary are unique sources of data for studying this type of language change as it happens; they are constantly updated and the threshold for including new material in them is lower than for traditional dictionaries. However, this comes with additional challenges. UD tends to record ephemeral quotidian spoken language and to represent popular views of meaning to a greater extent than other dictionaries. Slang and offensive language are over-represented in UD and its content often reflects the concerns of a specific community rather than wide linguistic trends. In this talk I will report on preliminary results of a study conducted in collaboration with Dr Dong Nguyen and Dr Taha Yasseri where we analysed UD data crawled in 2016 to answer the following questions:

- What is the distribution of entries in UD and how do they compare with Wiktionary? - To what extend can UD be a reliable source for recording lexical-semantic change in the English language?