Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

9-2021

Abstract

Semantic interpretability and visual expressivity are important objectives in exploratory analysis of text. On the one hand, while some documents may have explicit categories, we could develop a better understanding of a corpus by studying its finer-grained structures, which may be latent. By inferring latent topics and discovering keywords associated with each topic, one obtains a semantic interpretation of the corpus. One the other hand, by visualizing documents, latent topics, and category labels on the same plot, one gains a bird’s eye view of the relationships among documents, topics, and various categories. Semantic visualization is a class of methods that unify both topic modeling and visualization. In this paper, we propose a novel semantic visualization model for networked documents that incorporates partial labels. We introduce coordinate-based label distribution and label-dependent topic distribution to visualize documents, topics, and labels in a semi-supervised way. We further derive three variants for singly-labeled, multi-labeled, and hierarchically-labeled documents. The focus on semi-supervision that employs variants of labeling structures is particularly novel. Experiments verify the efficacy of our model against baselines.

Keywords

Dimensionality reduction, Generative models, Semantic visualization, Topic modeling

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering

Publication

Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17: Proceedings

Volume

12977

First Page

762

Last Page

778

ISBN

9783030865238

Identifier

10.1007/978-3-030-86523-8_46

Publisher

Springer

City or Country

Cham

Embargo Period

12-13-2021

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/978-3-030-86523-8_46

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