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.
No
paper
Topic Models; Hyper-parameters; Performance Evaluation
English
8th Italian Conference on Computational Linguistics, CLiC-it 2021
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.
Terragni, S; Fersini, E
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/363088
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