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Joint part-of-speech and dependency projection from multiple sources

机译:来自多个来源的联合词性和依赖项预测

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Most previous work on annotation projection has been limited to a subset of Indo-European languages, using only a single source language, and projecting annotation for one task at a time. In contrast, we present an Integer Linear Programming (ILP) algorithm that simultaneously projects annotation for multiple tasks from multiple source languages, relying on parallel corpora available for hundreds of languages. When training POS taggers and dependency parsers on jointly projected POS tags and syntactic dependencies using our algorithm, we obtain better performance than a standard approach on 20/23 languages using one parallel corpus; and 18/27 languages using another.
机译:以前有关注释投影的大多数工作仅限于一种印欧语言,仅使用一种源语言,并且一次为一个任务投影注释。相比之下,我们提出了一种整数线性编程(ILP)算法,该算法同时依赖于数百种语言可用的并行语料库,为来自多种源语言的多个任务同时投影注释。当使用我们的算法训练POS标记器和依存解析器以共同投影POS标记和句法依存关系时,与使用一种并行语料库的20/23语言的标准方法相比,我们可以获得比标准方法更好的性能;和18/27种语言使用另一种语言。

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