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Two-Stage Named-Entity Recognition Using Averaged Perceptrons

机译:使用verceptrons的两阶段命名实体识别

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We describe a simple approach to named-entity recognition (NER), aimed initially at the Dutch language, but potentially applicable to other languages. Our NER system employs a two-stage architecture, with handcrafted but dataset-independent features for both stages, and is on a par with state-of-the-art systems described in the literature. Notably, our approach does not depend on language-specific assets such as gazetteers. The resulting system is quite fast and is implemented in less than 500 lines of code.
机译:我们描述了一个简单的命名实体识别(ner)的方法,最初以荷兰语为目标,但可能适用于其他语言。我们的系统使用两级架构,具有手工制作,但对两个阶段的独立特征,并与文献中描述的最先进系统的相同。值得注意的是,我们的方法不依赖于宪报公报等语言特定资产。生成的系统非常快,并且在不到500行代码中实现。

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