Cross-grained context guided Chinese entity extraction with graph convolutional network
Yujie Li, Rujing Wang, Wei Li, Man Zhou, Yan Xiang Wu · 2020
Entity extraction is an attractive but challenging research filed. In recent years, the development of deep learning has promoted entity extraction techniques. However, owing to underutilizing multi-grained context, existing entity extraction methods obtain limited performance. Actually, cross-sentence information is capable of relieving the problem of ambiguity in entity extraction, especially for Chinese language. Instead of exploiting single intra-sentence context, we propose a cross-grained context guided Chinese entity extraction model by mining and integrating inter-sentence, intra-sentence and document-level information. Specifically, a document graph is constructed to simultaneously represent cross-grained information for a Chinese article using dependency syntax. Certain rules are designed to link syntactic trees of sentences to a weighted graph. Then an entity extraction model is established using GCN network to capture deep relations between words on the graph. Finally, cross-grained information embedded by GCN is used to predict entity mentions via multiclassification. Experiment results show that our method has achieved comparable performance to two baselines on Chinese discourse-level literature datasets, with average accuracy, recall, and F1 scores of 79.02%, 67.41%, and 78.08%, respectively.