Neural Models for Documents with Metadata

Code

• github.com/dallascard/neural_topic_models
  OfficialIn papernone★ 0
• github.com/dallascard/scholar
  OfficialIn paperpytorch★ 0
• github.com/maximilianahrens/scholar4regression
  tf★ 0

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.

Tasks

Reproductions