TY - JOUR AU - Tkachenko, Maksim AU - Lauw, Hady W. PY - 2019/07/17 Y2 - 2024/03/28 TI - CompareLDA: A Topic Model for Document Comparison JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 33 IS - 01 SE - AAAI Technical Track: Natural Language Processing DO - 10.1609/aaai.v33i01.33017112 UR - https://ojs.aaai.org/index.php/AAAI/article/view/4693 SP - 7112-7119 AB - <p>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 (<em>CompareLDA</em>) 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 <em>CompareLDA</em> in modelling document comparisons.</p> ER -