Dynamic Generative model for Diachronic Sense Emergence Detection

Abstract

As time passes words can acquire meanings they did not previously have, such as the `twitter post' usage of `tweet'. We address how this can be detected from time-stamped raw text. We propose a generative model with senses dependent on times and context words dependent on senses but otherwise eternal, and a Gibbs sampler for estimation. We obtain promising parameter estimates for positive (resp. negative) cases of known sense emergence (resp non-emergence) and adapt the `pseudo-word' technique (Schutze, 1992) to give a novel further evaluation via `pseudo-neologisms'. The question of ground-truth is also addressed and a technique proposed to locate an emergence date for evaluation purposes.

Tasks

Reproductions