Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

2-2019

Abstract

A number of real-world applications require comparison of entities based on their textual representations. In this work, we develop a topic model supervised by pairwise comparisons of documents. Such a model seeks to yield topics that help to differentiate entities along some dimension of interest, which may vary from one application to another. While previous supervised topic models consider document labels in an independent and pointwise manner, our proposed Comparative Latent Dirichlet Allocation (CompareLDA) learns predictive topic distributions that comply with the pairwise comparison observations. To fit the model, we derive a maximum likelihood estimation method via augmented variational approximation algorithm. Evaluation on several public datasets underscores the strengths of CompareLDA in modelling document comparisons.

Keywords

topic model, document comparison

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 33rd AAAI Conference on Artificial Intelligence 2019: Honolulu, January 27 - February 1

First Page

7112

Last Page

7119

Identifier

10.1609/aaai.v33i01.33017112

Publisher

AAAI Press

City or Country

Menlo Park, CA

Additional URL

https://doi.org/10.1609/aaai.v33i01.33017112

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