论文标题
超文本：用双曲线几何形状赋予快速文本
HyperText: Endowing FastText with Hyperbolic Geometry
论文作者
论文摘要
自然语言数据表现出类似树状的层次结构，例如WordNet中的超典型关系关系。 FastText是基于欧几里得空间中基于浅神经网络的最先进的文本分类器，可能不会以有限的表示能力来精确地建模此类层次结构。考虑到双曲线空间自然适合建模类层状层次数据，我们提出了一种名为Hypertext的新模型，用于通过赋予快速文本的高效文本分类，以使用双曲线几何形状。从经验上讲，我们表明超文本在一系列文本分类任务上胜过fastText，该任务大量降低了。
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.
