CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition
This addresses the problem of segmentation errors and out-of-vocabulary words in Chinese NER for NLP practitioners, offering a practical solution without external resources.
The paper tackles Chinese Named Entity Recognition (NER) by proposing a Convolutional Attention Network (CAN) that avoids reliance on word segmentation and external resources, achieving state-of-the-art performance on datasets like Weibo, MSRA, and Chinese Resume NER.
Named entity recognition (NER) in Chinese is essential but difficult because of the lack of natural delimiters. Therefore, Chinese Word Segmentation (CWS) is usually considered 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) words. In this paper, we investigate a Convolutional Attention Network called 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. Also, compared to other models, not depending on any external resources like lexicons and employing small size of char embeddings make our model more practical. Extensive experimental results show that our approach outperforms state-of-the-art methods without word embedding and external lexicon resources on different domain datasets including Weibo, MSRA and Chinese Resume NER dataset.