[PDF][PDF] Training and Evaluating a German Named Entity Recognizer with Semantic Generalization.
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.
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.
Abstract
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.
d-nb.info
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