ICML 2018oral6 citations
CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions
Kevin Tian, Teng Zhang, James Zou
Abstract
Word embedding is a useful approach to capture co-occurrence structures in large text corpora. However, in addition to the text data itself, we often have additional covariates associated with individual corpus documents—e.g. the demographic of the author, time and venue of publication—and we would like the embedding to naturally capture this information. We propose CoVeR, a new tensor decomposition model for vector embeddings with covariates. CoVeR jointly learns a
BibTeX
@InProceedings{pmlr-v80-tian18a,
title = {{C}o{V}e{R}: Learning Covariate-Specific Vector Representations with Tensor Decompositions},
author = {Tian, Kevin and Zhang, Teng and Zou, James},
booktitle = {Proceedings of the 35th International Conference on Machine Learning},
pages = {4926--4935},
year = {2018},
editor = {Dy, Jennifer and Krause, Andreas},
volume = {80},
series = {Proceedings of Machine Learning Research},
month = {10--15 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v80/tian18a/tian18a.pdf},
url = {https://proceedings.mlr.press/v80/tian18a.html},
abstract = {Word embedding is a useful approach to capture co-occurrence structures in large text corpora. However, in addition to the text data itself, we often have additional covariates associated with individual corpus documents—e.g. the demographic of the author, time and venue of publication—and we would like the embedding to naturally capture this information. We propose CoVeR, a new tensor decomposition model for vector embeddings with covariates. CoVeR jointly learns a