A Predictive Approach to Semantic Change Modelling

Mohamed Amine Boukhaled (1) , Benjamin Fagard (1) , Thierry Poibeau (1)

1 Lattice - Lattice - Langues, Textes, Traitements informatiques, Cognition - UMR 8094

Although it is well known that word meaning evolves over time, the cause and the pace of change is still largely unknown. In this context, computational modelling can shed new light on the problem by considering at the same time a large number of variables that are supposed to interact in the process. This field has already given birth to a large number of publications ranging from early work involving statistical and mathematical formalism (Bailey, 1973 ; Kroch, 1989) to more recent work involving robotics and large-scale simulations (Steels, 2011). We consider that semantic change includes all kinds of change in the meanings of lexical items happening over the years. In this work, we address the question of semantic change from a computational point of view. Our aim is to capture the systemic change of words meanings in an empirical model that is also predictive, contrary to most previous approaches that try to model and account for past data.

Mots clés: language evolution

Domaines: Traitement du texte et du document; Apprentissage [cs.LG]; Informatique; Linguistique; Linguistique; Méthodes et statistiques

Mohamed Amine Boukhaled, Benjamin Fagard, Thierry Poibeau. A Predictive Approach to Semantic Change Modelling. Digital Humanities, Jul 2019, Utrecht, Netherlands. ⟨hal-02265227⟩