Natural language data exhibit tree-like hierarchical structures such as the hypernym-hyponym relations in WordNet. FastText, as the state-of-the-art text classifier based on shallow neural network in Euclidean space, may not model such hierarchies precisely with limited representation capacity. Considering that hyperbolic space is naturally suitable for modeling tree-like hierarchical data, we propose a new model named HyperText for efficient text classification by endowing FastText with hyperbolic geometry. Empirically, we show that HyperText outperforms FastText on a range of text classification tasks with much reduced parameters.
@article{arxiv.2010.16143,
title = {HyperText: Endowing FastText with Hyperbolic Geometry},
author = {Yudong Zhu and Di Zhou and Jinghui Xiao and Xin Jiang and Xiao Chen and Qun Liu},
journal= {arXiv preprint arXiv:2010.16143},
year = {2021}
}