Exploiting Multiple Embeddings for Chinese Named Entity Recognition
Table 2. Experiment results and ablation study on Weibo NER and MSRA datasets. The best and second-best performance is indicated with black boldface and blue italics respectively. For the ablation study, we first remove radical embedding (marked as “-radical”), then replace Conv-GRU with CNN or BiLSTM (marked as “Conv-GRU→CNN” and “Conv-GRU→BiLSTM” respectively).
['Method', 'Weibo Pr', 'Weibo Re', 'Weibo F1', 'MSRA News Pr', 'MSRA News Re', 'MSRA News F1']
First, our use of multiple embeddings could encode semantic information towards NER from different perspectives. Second, the Conv-GRU networks work well in exploiting character-level information, which is extremely critical in Chinese NER task. Finally, the pre-trained embeddings are learned over large-scale news, crawled web pages and online novels, which provide further background knowledge. Note that all baselines also utilize the word and character embeddings pre-trained over a large external corpus. To examine this factor further, we train Lattice with Tencent embeddings. The results show that Tencent embeddings could further enhance the NER performance for Lattice. However, our proposed ME-CNER still outperforms it by 4.84 in terms of F1. We further conduct ablation study for ME-CNER by removing radical embeddings and replacing Conv-GRU network with either a CNN or a BiLSTM network.