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

Silvia Terragni, Debora Nozza, Elisabetta Fersini, Messina Enza


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.
Anthology ID:
2020.insights-1.5
Volume:
Proceedings of the First Workshop on Insights from Negative Results in NLP
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | insights
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
32–40
Language:
URL:
https://www.aclweb.org/anthology/2020.insights-1.5
DOI:
10.18653/v1/2020.insights-1.5
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.insights-1.5.pdf
Optional supplementary material:
 2020.insights-1.5.OptionalSupplementaryMaterial.zip