Abstract
Conventional word embedding models do not leverage information from document meta-data, and they do not model uncertainty. We address these concerns with a model that incorporates document covariates to estimate conditional word embedding distributions. Our model allows for (a) hypothesis tests about the meanings of terms, (b) assessments as to whether a word is near or far from another conditioned on different covariate values, and (c) assessments as to whether estimated differences are statistically significant.- Anthology ID:
- D18-1527
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4890–4895
- Language:
- URL:
- https://aclanthology.org/D18-1527
- DOI:
- 10.18653/v1/D18-1527
- Cite (ACL):
- Rujun Han, Michael Gill, Arthur Spirling, and Kyunghyun Cho. 2018. Conditional Word Embedding and Hypothesis Testing via Bayes-by-Backprop. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4890–4895, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Conditional Word Embedding and Hypothesis Testing via Bayes-by-Backprop (Han et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/D18-1527.pdf