Multilingual Topic Labelling of News Topics Using Ontological Mapping

Abstract

The large volume of news produced daily makes topic modelling useful for analysing topical trends. A topic is usually represented by a ranked list of words but this can be difficult and time-consuming for humans to interpret. Therefore, various methods have been proposed to generate labels that capture the semantic content of a topic. However, there has been no work so far on coming up with multilingual labels which can be useful for exploring multilingual news collections. We propose an ontological mapping method that maps topics to concepts in a language-agnostic news ontology. We test our method on Finnish and English topics and show that it performs on par with state-of-the-art label generation methods, is able to produce multilingual labels, and can be applied to topics from languages that have not been seen during training without any modifications.