We compare three simple and popular approaches for NER: 1) SEQ (sequence-labeling with a linear token classifier) 2) SeqCRF (sequence-labeling with Conditional Random Fields), and 3) SpanPred (span-prediction with boundary token embeddings). We compare the approaches on 4 biomedical NER tasks: GENIA, NCBI-Disease, LivingNER (Spanish), and SocialDisNER (Spanish). The SpanPred model demonstrates state-of-the-art performance on LivingNER and SocialDisNER, improving F1 by 1.3 and 0.6 F1 respectively. The SeqCRF model also demonstrates state-of-the-art performance on LivingNER and SocialDisNER, improving F1 by 0.2 F1 and 0.7 respectively. The SEQ model is competitive with the state-of-the-art on the LivingNER dataset. We explore some simple ways of combining the three approaches. We find that majority voting consistently gives high precision and high F1 across all 4 datasets. Lastly, we implement a system that learns to combine the predictions of SEQ and SpanPred, generating systems that consistently give high recall and high F1 across all 4 datasets. On the GENIA dataset, we find that our learned combiner system significantly boosts F1(+1.2) and recall(+2.1) over the systems being combined. We release all the well-documented code necessary to reproduce all systems at https://github.com/flyingmothman/bionlp.

翻译：我们比较了三种简单且流行的命名实体识别方法：1) SEQ（基于线性标记分类器的序列标注），2) SeqCRF（基于条件随机场的序列标注），以及3) SpanPred（基于边界标记嵌入的跨度预测）。我们在四个生物医学命名实体识别任务上比较了这些方法：GENIA、NCBI-Disease、LivingNER（西班牙语）和SocialDisNER（西班牙语）。SpanPred模型在LivingNER和SocialDisNER上展现了最先进的性能，分别将F1值提升了1.3和0.6。SeqCRF模型在LivingNER和SocialDisNER上也展现了最先进的性能，分别将F1值提升了0.2和0.7。SEQ模型在LivingNER数据集上与最先进水平相当。我们探索了组合这三种方法的一些简单方式，发现多数投票法在所有四个数据集上均能稳定获得高精确率和高F1值。最后，我们实现了一个学习组合SEQ和SpanPred预测结果的系统，生成的系统在所有四个数据集上均能稳定获得高召回率和高F1值。在GENIA数据集上，我们发现学习组合器系统相较于被组合的系统，显著提升了F1值（+1.2）和召回率（+2.1）。我们已在https://github.com/flyingmothman/bionlp上发布了复现所有系统所需的完整文档化代码。