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Most state-of-the-art named entity recognition (NER) systems rely onhandcrafted features and on the output of other NLP tasks such aspart-of-speech (POS) tagging and text chunking. In this work we propose alanguage-independent NER system that uses automatically learned features only.Our approach is based on the CharWNN deep neural network, which uses word-leveland character-level representations (embeddings) to perform sequentialclassification. We perform an extensive number of experiments using twoannotated corpora in two different languages: HAREM I corpus, which containstexts in Portuguese; and the SPA CoNLL-2002 corpus, which contains texts inSpanish. Our experimental results shade light on the contribution of neuralcharacter embeddings for NER. Moreover, we demonstrate that the same neuralnetwork which has been successfully applied to POS tagging can also achievestate-of-the-art results for language-independet NER, using the samehyperparameters, and without any handcrafted features. For the HAREM I corpus,CharWNN outperforms the state-of-the-art system by 7.9 points in the F1-scorefor the total scenario (ten NE classes), and by 7.2 points in the F1 for theselective scenario (five NE classes).

Boosting Named Entity Recognition with Neural Character Embeddings