Skip to Main content Skip to Navigation
Conference papers

Readitopics: Make Your Topic Models Readable via Labeling and Browsing

Abstract : Readitopics provides a new tool for browsing a textual corpus that showcases several recent work for labeling topic models and estimating topic coherence. We will demonstrate the potential of these techniques to get a deeper understanding of the topics that structure different kinds of datasets. This tool is provided as a Web demo but it can be easily installed to experiment with your own dataset. It can be further extended to deal with more advanced topic modeling techniques.
Complete list of metadatas

Cited literature [12 references]  Display  Hide  Download

https://hal-lirmm.ccsd.cnrs.fr/lirmm-01910611
Contributor : Pascal Poncelet <>
Submitted on : Thursday, November 1, 2018 - 3:18:14 PM
Last modification on : Tuesday, March 17, 2020 - 2:56:00 AM
Document(s) archivé(s) le : Saturday, February 2, 2019 - 1:33:21 PM

File

readitopics2018.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : lirmm-01910611, version 1

Citation

Julien Velcin, Antoine Gourru, Erwan Giry-Fouquet, Christophe Gravier, Mathieu Roche, et al.. Readitopics: Make Your Topic Models Readable via Labeling and Browsing. IJCAI: International Joint Conference on Artificial Intelligence, Jul 2018, Stockholm, Sweden. ⟨lirmm-01910611⟩

Share

Metrics

Record views

270

Files downloads

260