We present a multilingual Named Entity Recognition approach based on a robust and general set of features across languages and datasets. Our system combines shallow local information with clustering semi-supervised features induced on large amounts of unlabeled text. Understanding via empirical experimentation how to effectively combine various types of clustering features allows us to seamlessly export our system to other datasets and languages. The result is a simple but highly competitive system which obtains state of the art results across five languages and twelve datasets. The results are reported on standard shared task evaluation data such as CoNLL for English, Spanish and Dutch. Furthermore, and despite the lack of linguistically motivated features, we also report best results for languages such as Basque and German. In addition, we demonstrate that our method also obtains very competitive results even when the amount of supervised data is cut by half, alleviating the dependency on manually annotated data. Finally, the results show that our emphasis on clustering features is crucial to develop robust out-of-domain models. The system and models are freely available to facilitate its use and guarantee the reproducibility of results.

通过结合浅层局部信息和聚类半监督特征，我们提出了一种基于跨语言和数据集的稳健性和通用性的多语言实体识别方法，该方法可以无缝地导出到其他数据集和语言中。该系统展示出领先水平，在五种语言和12个数据集中取得了最好的结果，并且无需有语言学动机的特征，也可以在巴斯克语和德语等语言中获得最佳结果，同时削减半监督数据依赖性，对于开发强大的域外模型，聚类特征的重点至关重要。

具有浅层半监督特征的强大多语言命名实体识别