@inproceedings{zhu-etal-2020-hypertext,
title = "{H}yper{T}ext: Endowing {F}ast{T}ext with Hyperbolic Geometry",
author = "Zhu, Yudong and
Zhou, Di and
Xiao, Jinghui and
Jiang, Xin and
Chen, Xiao and
Liu, Qun",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.104/",
doi = "10.18653/v1/2020.findings-emnlp.104",
pages = "1166--1171",
abstract = "Natural language data exhibit tree-like hierarchical structures such as the hypernym-hyponym hierarchy in WordNet. FastText, as the state-of-the-art text classifier based on shallow neural network in Euclidean space, may not represent such hierarchies precisely with limited representation capacity. Considering that hyperbolic space is naturally suitable for modelling 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."
}
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<abstract>Natural language data exhibit tree-like hierarchical structures such as the hypernym-hyponym hierarchy in WordNet. FastText, as the state-of-the-art text classifier based on shallow neural network in Euclidean space, may not represent such hierarchies precisely with limited representation capacity. Considering that hyperbolic space is naturally suitable for modelling 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.</abstract>
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%0 Conference Proceedings
%T HyperText: Endowing FastText with Hyperbolic Geometry
%A Zhu, Yudong
%A Zhou, Di
%A Xiao, Jinghui
%A Jiang, Xin
%A Chen, Xiao
%A Liu, Qun
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F zhu-etal-2020-hypertext
%X Natural language data exhibit tree-like hierarchical structures such as the hypernym-hyponym hierarchy in WordNet. FastText, as the state-of-the-art text classifier based on shallow neural network in Euclidean space, may not represent such hierarchies precisely with limited representation capacity. Considering that hyperbolic space is naturally suitable for modelling 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.
%R 10.18653/v1/2020.findings-emnlp.104
%U https://aclanthology.org/2020.findings-emnlp.104/
%U https://doi.org/10.18653/v1/2020.findings-emnlp.104
%P 1166-1171
Markdown (Informal)
[HyperText: Endowing FastText with Hyperbolic Geometry](https://aclanthology.org/2020.findings-emnlp.104/) (Zhu et al., Findings 2020)
ACL
- Yudong Zhu, Di Zhou, Jinghui Xiao, Xin Jiang, Xiao Chen, and Qun Liu. 2020. HyperText: Endowing FastText with Hyperbolic Geometry. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1166–1171, Online. Association for Computational Linguistics.