Unsupervised Morphological Segmentation for Low-Resource Polysynthetic Languages
Unsupervised Morphological Segmentation for Low-Resource Polysynthetic Languages
复制标题
低资源多合成语言的无监督形态分割
DOI：
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发表时间：
2019
期刊：
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通讯作者：
S. Muresan
中科院分区：
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作者：
R. Eskander;Judith L. Klavans;S. Muresan
多合成语言由于词根语素的复杂性和词类“压扁”给词形分析带来了挑战。此外，许多多合成语言资源匮乏。我们提出了基于Adaptor Grammars （AG）的低资源多合成语言形态分割的无监督方法（Eskander et al., 2016）。我们用乌托-阿兹特克语系的四种语言做实验。我们基于ag的方法优于其他无监督的方法，并且与有监督的方法相比显示出希望，在四种语言中的两种上都优于它们。
Polysynthetic languages pose a challenge for morphological analysis due to the root-morpheme complexity and to the word class “squish”. In addition, many of these polysynthetic languages are low-resource. We propose unsupervised approaches for morphological segmentation of low-resource polysynthetic languages based on Adaptor Grammars (AG) (Eskander et al., 2016). We experiment with four languages from the Uto-Aztecan family. Our AG-based approaches outperform other unsupervised approaches and show promise when compared to supervised methods, outperforming them on two of the four languages.