In this paper, we present OCTIS, a framework for training, analyzing, and comparing Topic Models, whose optimal hyper-parameters are estimated using a Bayesian Optimization approach. The proposed solution integrates several state-of-the-art topic models and evaluation metrics. These metrics can be targeted as objective by the underlying optimization procedure to determine the best hyper-parameter configuration. OCTIS allows researchers and practitioners to have a fair comparison between topic models of interest, using several benchmark datasets and well-known evaluation metrics, to integrate novel algorithms, and to have an interactive visualization of the results for understanding the behavior of each model. The code is available at the following link: https://github.com/MIND-Lab/OCTIS.

Terragni, S., Fersini, E., Galuzzi, B., Tropeano, P., Candelieri, A. (2021). OCTIS: Comparing and optimizing topic models is simple!. In EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the System Demonstrations (pp.263-270). Association for Computational Linguistics (ACL).

OCTIS: Comparing and optimizing topic models is simple!

Terragni, S;Fersini, E;Galuzzi, B;Candelieri, A
2021

Abstract

In this paper, we present OCTIS, a framework for training, analyzing, and comparing Topic Models, whose optimal hyper-parameters are estimated using a Bayesian Optimization approach. The proposed solution integrates several state-of-the-art topic models and evaluation metrics. These metrics can be targeted as objective by the underlying optimization procedure to determine the best hyper-parameter configuration. OCTIS allows researchers and practitioners to have a fair comparison between topic models of interest, using several benchmark datasets and well-known evaluation metrics, to integrate novel algorithms, and to have an interactive visualization of the results for understanding the behavior of each model. The code is available at the following link: https://github.com/MIND-Lab/OCTIS.
slide + paper
Natural Language Processing, Topic Models, Hyperparameter Optimization, Bayesian Optimization
English
16th Conference of the European Chapter of the Associationfor Computational Linguistics: System Demonstrations, EACL 2021 - 19 April 2021 through 23 April 2021
2021
EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the System Demonstrations
9781954085053
2021
263
270
none
Terragni, S., Fersini, E., Galuzzi, B., Tropeano, P., Candelieri, A. (2021). OCTIS: Comparing and optimizing topic models is simple!. In EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the System Demonstrations (pp.263-270). Association for Computational Linguistics (ACL).
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/324190
Citazioni
  • Scopus 52
  • ???jsp.display-item.citation.isi??? 30
Social impact