Five-Stroke Based CNN-BiRNN-CRF Network for Chinese Named Entity Recognition

Abstract

Identifying entity boundaries and eliminating entity ambiguity are two major challenges faced by Chinese named entity recognition researches. This paper proposes a five-stroke based CNN-BiRNN-CRF network for Chinese named entity recognition. In terms of input embeddings, we apply five-stroke input method to obtain stroke-level representations, which are concatenated with pre-trained character embeddings, in order to explore the morphological and semantic information of characters. Moreover, the convolutional neural network is used to extract n-gram features, without involving hand-crafted features or domain-specific knowledge. The proposed model is evaluated and compared with the state-of-the-art results on the third SIGHAN bakeoff corpora. The experimental results show that our model achieves 91.67% and 90.68% F1-score on MSRA corpus and CityU corpus separately.