Ibai Guillén-Pacho

Campus de Montegancedo, s/n. ETSIINF, Office: 3206

28660, Boadilla del Monte

Madrid, Spain

I am Ibai Guillén Pacho, a graduate in Computer Engineering and Digital Business Transformation from the University of Deusto. My passion for Artificial Intelligence led me to Madrid, where I completed a specialized master’s degree in this field in the Universidad Politécnica de Madrid. I decided to delve even deeper into the field, and I am currently immersed in an exciting doctoral program in Artificial Intelligence at the same institution. My research focuses on many tasks, but all related to knowledge temporal evolution. More specifically, I have worked with Dynamic Topic Models, Ontologies, Knowledge Graphs, Disinformation, Political Discourse and Hate Speech.

I have accounts on different platforms that you can see at the bottom of the page, where you can see my activity. However, if you want to contact me my preferred option is the email.

Selected Publications

Dynamic topic modelling for exploring the scientific literature on coronavirus: an unsupervised labelling technique

Ibai Guillén-Pacho, Carlos Badenes-Olmedo, and Oscar Corcho

International Journal of Data Science and Analytics, Aug 2024

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The work presented in this article focusses on improving the interpretability of probabilistic topic models created from a large collection of scientific documents that evolve over time. Several time-dependent approaches based on topic models were compared to analyse the annual evolution of latent concepts in the CORD-19 corpus: Dynamic Topic Model, Dynamic Embedded Topic Model, and BERTopic. Then COVID-19 period (December 2019–present) has been analysed in greater depth, month by month, to explore the evolution of what is written about the disease. The evaluations suggest that the Dynamic Topic Model is the best choice to analyse the CORD-19 corpus. A novel topic labelling strategy is proposed for dynamic topic models to analyse the evolution of latent concepts. It incorporates content changes in both the annual evolution of the corpus and the monthly evolution of the COVID-19 disease. The generated labels are manually validated using two approaches: through the most relevant documents on the topic and through the documents that share the most semantically similar label topics. The labelling enables the interpretation of topics. The novel method for dynamic topic labelling fits the content of each topic and supports the semantics of the topics.