Distributional modelling for semantic shift detection. (10th July 2018) - Fišer, Darja; Ljubešić, Nikola
Abstract: This paper gives an overview of distributional modelling of word meaning for contemporary lexicography. We also apply it in a case study on automatic semantic shift detection in Slovene tweets. We use word embeddings to compare the semantic behaviour of frequent words from a reference corpus of Slovene with their behaviour on Twitter. Words with the highest model distance between the corpora are considered as semantic shift candidates. They are manually analysed and classified in order to evaluate the proposed approach as well as to gain a better qualitative understanding of the problem. Apart from the noise due to pre-processing errors (45%), the approach yields a lot of valuable candidates, especially the novel senses occurring due to daily events and the ones produced in informal communication settings.