Named entity recognition (NER) is a common task in Natural Language Processing (NLP). but it remains more challenging in Chinese because of its lack of natural delimiters. Therefore, Chinese Word Segmentation (CWS) is usually necessary as the first step for Chinese NER. However, models based on word-level embeddings and lexicon features often suffer from segmentation errors and out-of-vocabulary (OOV) problems. In this paper, we investigate a Convolutional Attention Network (CAN) for Chinese NER, which consists of a character-based convolutional neural network (CNN) with local-attention layer and a gated recurrent unit (GRU) with global self-attention layer to capture the information from adjacent characters and sentence contexts. Moreover, differently from other approaches, CAN-NER does not depend on any external resources like lexicons and employing small-size char em-beddings makes CAN-NER more practical for real systems scenarios. Extensive experimental results show that our approach outperforms state-of-the-art methods without word embedding and external lexicon resources on different domains datasets.
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