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.
| File | Dimensione | Formato | |
|---|---|---|---|
|
10281-298207_VoR.pdf
accesso aperto
Tipologia di allegato:
Publisher’s Version (Version of Record, VoR)
Licenza:
Creative Commons
Dimensione
355.24 kB
Formato
Adobe PDF
|
355.24 kB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
