In this paper we present a system that exploits different pre-trained Language Models for assigning domain labels to WordNet synsets without any kind of supervision. Furthermore, the system is not restricted to use a particular set of domain labels.
We exploit the knowledge encoded within different off-the-shelf pre-trained Language Models and task formulations to infer the domain label of a particular WordNet definition. The proposed zero-shot system achieves a new state-of-the-art on the English dataset used in the evaluation.
This paper describes the development of an online lexical resource to help detection systems regulate and curb the use of offensive words online. With the growing prevalence of social media platforms, many conversations are now conducted on- line. Th
e increase of online conversations for leisure, work and socializing has led to an increase in harassment. In particular, we create a specialized sense-based vocabulary of Japanese offensive words for the Open Multilingual Wordnet. This vocabulary expands on an existing list of Japanese offen- sive words and provides categorization and proper linking to synsets within the multilingual wordnet. This paper then discusses the evaluation of the vocabulary as a resource for representing and classifying offensive words and as a possible resource for offensive word use detection in social media.
