Infinite SCAN: An Infinite Model of Diachronic Semantic Change

Seiichi Inoue, Mamoru Komachi, Toshinobu Ogiso, Hiroya Takamura, Daichi Mochihashi

Abstract: In this study, we propose a Bayesian model that can jointly estimate the number of senses of words and their changes through time. The model combines a dynamic topic model on Gaussian Markov random fields (Frermann and Lapata, 2016) with a logistic stick-breaking process that realizes the Dirichlet process. In the experiments, we evaluated the proposed model in terms of interpretability, accuracy in estimating the number of senses, and track- ing their changes using both artificial data and real data. We quantitatively verified that the model behaves as expected through evaluation using artificial data. Using the CCOHA cor- pus, we showed that our model outperforms the baseline model and investigated the semantic changes of several well-known target words.