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.- Anthology ID:
- P18-1189
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2031–2040
- Language:
- URL:
- https://aclanthology.org/P18-1189
- DOI:
- 10.18653/v1/P18-1189
- Cite (ACL):
- Dallas Card, Chenhao Tan, and Noah A. Smith. 2018. Neural Models for Documents with Metadata. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2031–2040, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Neural Models for Documents with Metadata (Card et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/P18-1189.pdf
- Code
- dallascard/scholar + additional community code
- Data
- IMDb Movie Reviews