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.
The best German CoNLL 2003 system (an ensemble) was outperformed by @cite_11 . They trained the Stanford NER system , which uses a linear-chain Conditional Random Field (CRF) with a variety of features, including lemma, POS tag, etc. Crucially, they included distributional similarity'' features in the form of Clark clusters induced from large unlabeled corpora: the Huge German Corpus (HGC) of around 175M tokens of newspaper text and the deWac corpus consisting of 1.71B tokens of web-crawled data. Using the clusters induced from deWac as a form of improved the results over the best CoNLL 2003 system by 4 points in F1.
@cite_11
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We present a freely available optimized Named Entity Recognizer (NER) for German. It alleviates the small size of available NER training corpora for German with distributional generalization features trained on large unlabelled corpora. We vary the size and source of the generalization corpus and find improvements of 6 F1 score (in-domain) and 9 (out-of-domain) over simple supervised training.