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2025

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Beyond Raw Text: Knowledge-Augmented Italian Relation Extraction with Large Language Models
Gianmaria Balducci | Elisabetta Fersini | Messina Enza
Proceedings of the Eleventh Italian Conference on Computational Linguistics (CLiC-it 2025)

2020

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Which Matters Most? Comparing the Impact of Concept and Document Relationships in Topic Models
Silvia Terragni | Debora Nozza | Elisabetta Fersini | Messina Enza
Proceedings of the First Workshop on Insights from Negative Results in NLP

Topic models have been widely used to discover hidden topics in a collection of documents. In this paper, we propose to investigate the role of two different types of relational information, i.e. document relationships and concept relationships. While exploiting the document network significantly improves topic coherence, the introduction of concepts and their relationships does not influence the results both quantitatively and qualitatively.
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