OCTIS is an open-source framework for training, evaluating and comparing Topic Models. This tool uses single-objective Bayesian Optimization (BO) to optimize the hyper-parameters of the models and thus guarantee a fairer comparison. Yet, a single-objective approach disregards that a user may want to simultaneously optimize multiple objectives. We therefore propose OCTIS 2.0: the extension of OCTIS that addresses the problem of estimating the optimal hyper-parameter configurations for a topic model using multi-objective BO. Moreover, we also release and integrate two pre-processed Italian datasets, which can be easily used as benchmarks for the Italian language.
Terragni, S., Fersini, E. (2021). OCTIS 2.0: Optimizing and comparing topic models in Italian is even simpler!. In Proceedings of the Eighth Italian Conference on Computational Linguistics (CLiC-it 2021), Milan, Italy, January 26-28, 2022 (pp.1-7). CEUR-WS.
OCTIS 2.0: Optimizing and comparing topic models in Italian is even simpler!
Terragni, S
;Fersini, E
2021
Abstract
OCTIS is an open-source framework for training, evaluating and comparing Topic Models. This tool uses single-objective Bayesian Optimization (BO) to optimize the hyper-parameters of the models and thus guarantee a fairer comparison. Yet, a single-objective approach disregards that a user may want to simultaneously optimize multiple objectives. We therefore propose OCTIS 2.0: the extension of OCTIS that addresses the problem of estimating the optimal hyper-parameter configurations for a topic model using multi-objective BO. Moreover, we also release and integrate two pre-processed Italian datasets, which can be easily used as benchmarks for the Italian language.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
