CASE: Large Scale Topic Exploitation for Decision Support Systems
Lorena Calvo Bartolomé, Jerónimo Arenas-García, David Pérez Fernández
Abstract
In recent years, there has been growing interest in using NLP tools for decision support systems, particularly in Science, Technology, and Innovation (STI). Among these, topic modeling has been widely used for analyzing large document collections, such as scientific articles, research projects, or patents, yet its integration into decision-making systems remains limited. This paper introduces CASE, a tool for exploiting topic information for semantic analysis of large corpora. The core of CASE is a Solr engine with a customized indexing strategy to represent information from Bayesian and Neural topic models that allow efficient topic-enriched searches. Through ad-hoc plug-ins, CASE enables topic inference on new texts and semantic search. We demonstrate the versatility and scalability of CASE through two use cases: the calculation of aggregated STI indicators and the implementation of a web service to help evaluate research projects.
BibTeX
@inproceedings{calvo-bartolome-etal-2025-case,
title = "{CASE}: Large Scale Topic Exploitation for Decision Support Systems",
author = "Calvo Bartolom{\'e}, Lorena and
Arenas-Garc{\'i}a, Jer{\'o}nimo and
P{\'e}rez Fern{\'a}ndez, David",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven and
Mather, Brodie and
Dras, Mark",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics: System Demonstrations",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-demos.15/",
pages = "151--162"
}