CompareLDA: A Topic Model for Document Comparison

Authors

  • Maksim Tkachenko Singapore Management University
  • Hady W. Lauw Singapore Management University

DOI:

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

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.

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Published

2019-07-17

How to Cite

Tkachenko, M., & Lauw, H. W. (2019). CompareLDA: A Topic Model for Document Comparison. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 7112-7119. https://doi.org/10.1609/aaai.v33i01.33017112

Issue

Section

AAAI Technical Track: Natural Language Processing