Named Entity Recognition with Gated Convolutional Neural Networks

Most state-of-the-art models for named entity recognition (NER) rely on recurrent neural networks (RNNs), in particular long short-term memory (LSTM). Those models learn local and global features automatically by RNNs so that hand-craft features can be discarded, totally or partly. Recently, convolutional neural networks (CNNs) have achieved great success on computer vision. However, for NER problems, they are not well studied. In this work, we propose a novel architecture for NER problems based on GCNN — CNN with gating mechanism. Compared with RNN based NER models, our proposed model has a remarkable advantage on training efficiency. We evaluate the proposed model on three data sets in two significantly different languages — SIGHAN bakeoff 2006 MSRA portion for simplified Chinese NER and CityU portion for traditional Chinese NER, CoNLL 2003 shared task English portion for English NER. Our model obtains state-of-the-art performance on these three data sets.