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arXiv · 1705.09296

Neural Models for Documents with Metadata

Dallas Card·Chenhao Tan·Noah A. Smith
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Abstract

Most real-world document collections involve various types of metadata, such as author, source, and date, and yet the most commonly-used approaches to modeling text corpora ignore this information. While specialized models have been developed for particular applications, few are widely used in practice, as customization typically requires derivation of a custom inference algorithm. In this paper, we build on recent advances in variational inference methods and propose a general neural framework, based on topic models, to enable flexible incorporation of metadata and allow for rapid exploration of alternative models. Our approach achieves strong performance, with a manageable tradeoff between perplexity, coherence, and sparsity. Finally, we demonstrate the potential of our framework through an exploration of a corpus of articles about US immigration.

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BibTeXRIS

Dallas Card, Chenhao Tan, Noah A. Smith. 2018-10-23. Neural Models for Documents with Metadata. https://doi.org/10.18653/v1%2Fp18-1189

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