Annotation Scheme and Evaluation: The Case of Offensive Language

Abstract

The present paper focuses on the presentation and discussion of aspects of offensive lan-guage linguistic annotation, including the creation, annotation practice, curation, and evaluation of an offensive language annotation taxonomy scheme, that was first proposed in Lewandowska-Tomaszczyk et al. (2021). An extended offensive language ontology comprising 17 categories, structured in terms of 4 hierarchical levels, has been shown to represent the encoding of the defined offensive language schema, trained in terms of non-contextual word embeddings – i.e., Word2Vec and Fast Text, and eventually juxtaposed to the data acquired by using a pair wise training and testing analysis for existing categories in the HateBERT model (Lewandowska-Tomaszczyk et al. submitted). The study reports on the annotation practice in WG 4.1.1. Incivility in media and social media in the context of COST Action CA 18209 European network for Web-centred linguistic data science (Nexus Linguarum) with the INCEpTION tool (https://github.com/inception-project/incep-tion) – a semantic annotation platform offering assistance in the annotation. The results partly support the proposed ontology of explicit offense and positive implicitness types to provide more variance among widely recognized types of figurative language (e.g., meta-phorical, metonymic, ironic, etc.). The use of the annotation system and the representation of linguistic data were also evaluated in a series of the annotators’ comments, by means of a questionnaire and an open discussion. The annotation results and the questionnaire showed that for some of the categories there was low or medium inter-annotator agreement, and it was more challenging for annotators to distinguish between category items than between aspect items, with the category items offensive, insulting and abusive being the most difficult in this respect. The need for taxonomic simplification measureson the basis of these results has been recognized for further annotation practices.