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.

Terragni, S., Nozza, D., Fersini, E., Messina, V. (2020). Which Matters Most? Comparing the Impact of Concept and Document Relationships in Topic Models. In Proceedings of the First Workshop on Insights from Negative Results in NLP (pp.32-40). Association for Computational Linguistics [10.18653/v1/2020.insights-1.5].

Which Matters Most? Comparing the Impact of Concept and Document Relationships in Topic Models

Terragni, S;Nozza, D;Fersini, E;Messina, V
2020

Abstract

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.
paper
Relational Topic Models
English
First Workshop on Insights from Negative Results in NLP
2020
Proceedings of the First Workshop on Insights from Negative Results in NLP
2020
32
40
https://www.aclweb.org/anthology/2020.insights-1.5
none
Terragni, S., Nozza, D., Fersini, E., Messina, V. (2020). Which Matters Most? Comparing the Impact of Concept and Document Relationships in Topic Models. In Proceedings of the First Workshop on Insights from Negative Results in NLP (pp.32-40). Association for Computational Linguistics [10.18653/v1/2020.insights-1.5].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/298207
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