Lorena Calvo Bartolomé


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ITMT: Interactive Topic Model Trainer
Lorena Calvo Bartolomé | José Antonio Espinosa Melchor | Jerónimo Arenas-garcía
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

Topic Modeling is a commonly used technique for analyzing unstructured data in various fields, but achieving accurate results and useful models can be challenging, especially for domain experts who lack the knowledge needed to optimize the parameters required by this natural language processing technique. From this perspective, we introduce an Interactive Topic Model Trainer (ITMT) developed within the EU-funded project IntelComp. ITMT is a user-in-the-loop topic modeling tool presented with a graphical user interface that allows the training and curation of different state-of-the-art topic extraction libraries, including some recent neural-based methods, oriented toward the usage by domain experts. This paper reviews ITMT’s functionalities and key implementation aspects in this paper, including a comparison with other tools for topic modeling analysis.