Dsolve - Morphological Segmentation for German Using Conditional Random Fields
Dsolve - Morphological Segmentation for German Using Conditional Random Fields
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DOI：
10.1007/978-3-319-23980-4_6
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发表时间：
2015-09
期刊：
影响因子：
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通讯作者：
Kay-Michael Würzner;Bryan Jurish
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文献类型：
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作者：
Kay-Michael Würzner;Bryan Jurish
我们描述Dsolve，一个系统的分割形态复杂的德语单词到其组成的变体。我们的方法将形态分割作为一个分类任务，其中变形边界的位置和类型是由一个条件随机场模型从手动注释的数据训练预测。除了它们的位置预测的形态边界类型区分Dsolve从类似的方法以前在文献中提出。我们表明，使用边界类型提供了一个（有点违反直觉）的性能提升相对于简单的任务，只预测段的位置。
We describe Dsolve, a system for the segmentation of morphologically complex German words into their constituent morphs. Our approach treats morphological segmentation as a classification task, in which the locations and types of morph boundaries are predicted by a Conditional Random Field model trained from manually annotated data. The prediction of morph-boundary types in addition to their locations distinguishes Dsolve from similar approaches previously suggested in the literature. We show that the use of boundary types provides a (somewhat counter-intuitive) performance boost with respect to the simpler task of predicting only segment locations.