Information Retrieval Journal

, Volume 18, Issue 4, pp 283–330 | Cite as

Supervised topic models with word order structure for document classification and retrieval learning

Article
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Abstract

One limitation of most existing probabilistic latent topic models for document classification is that the topic model itself does not consider useful side-information, namely, class labels of documents. Topic models, which in turn consider the side-information, popularly known as supervised topic models, do not consider the word order structure in documents. One of the motivations behind considering the word order structure is to capture the semantic fabric of the document. We investigate a low-dimensional latent topic model for document classification. Class label information and word order structure are integrated into a supervised topic model enabling a more effective interaction among such information for solving document classification. We derive a collapsed Gibbs sampler for our model. Likewise, supervised topic models with word order structure have not been explored in document retrieval learning. We propose a novel supervised topic model for document retrieval learning which can be regarded as a pointwise model for tackling the learning-to-rank task. Available relevance assessments and word order structure are integrated into the topic model itself. We conduct extensive experiments on several publicly available benchmark datasets, and show that our model improves upon the state-of-the-art models.

Keywords

Topic modeling Maximum-margin Document classification Learning-to-rank Structured topic model 
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Notes

Acknowledgments

The work described in this paper is substantially supported by Grants from the Research Grant Council of the Hong Kong Special Administrative Region, China (Project Codes: 413510 and 14203414) and the Direct Grant of the Faculty of Engineering, CUHK (Project Code: 4055034). This work is also affiliated with the CUHK MoE-Microsoft Key Laboratory of Human-centric Computing and Interface Technologies. The authors would like to thank anonymous reviewers for their comments and suggestions.

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© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Systems Engineering and Engineering ManagementThe Chinese University of Hong KongShatinHong Kong
  2. 2.Machine Learning DepartmentCarnegie Mellon UniversityPittsburghUSA

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