Abstract

Named Entity Recognition is a relatively well-understood NLP task, with many publicly available training resources and software for English. Other languages tend to be underserved in this area. For German, CoNLL-2003 provides training data, but there are no publicly available, ready-to-use tools. We fill this gap and develop a German NER system with state-of-the-art performance. In addition to CoNLL 2003 labeled training data, we use two additional resources: (i) 32 million words of unlabeled text and (ii) infobox labels in German Wikipedia articles. We extract informative features of word-types from those resources and train a supervised model on the labeled training data. This approach allows us to deal better with word-types unseen in the training data and achieve state-of-the-art performance on German with little engineering effort. 1.

Citations