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In this paper, we present a novel neural\nnetwork architecture that automatically detects word- and character-level\nfeatures using a hybrid bidirectional LSTM and CNN architecture, eliminating\nthe need for most feature engineering. We also propose a novel method of\nencoding partial lexicon matches in neural networks and compare it to existing\napproaches. Extensive evaluation shows that, given only tokenized text and\npublicly available word embeddings, our system is competitive on the CoNLL-2003\ndataset and surpasses the previously reported state of the art performance on\nthe OntoNotes 5.0 dataset by 2.13 F1 points. 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