用有限状态自动机实现更高效准确的断续实体识别
A fast and sound tagging method for discontinuous named-entity recognition
- 基于显式结构描述设计标签方案，利用加权自动机进行推理
- 在三个生物医学数据集上达到顶尖水平，速度显著更快
- 适合需要高精度断续实体识别的医疗文本分析场景
我们提出一种基于断续提及内部结构显式描述的新型命名实体识别标签方法。该方法依赖加权有限状态自动机进行边缘推断和最大后验推断。因此，该方法在理论上是可靠的：（1）通过自动机结构确保预测标签序列的合法性；（2）良好形成的标签序列与断续实体之间存在明确的一一映射关系。我们在三个英文生物医学数据集上评估了该方法，在性能上与当前最优模型相当，但模型更简单、运行更快。
原文摘要 · Abstract (English)
We introduce a novel tagging scheme for discontinuous named entity recognition based on an explicit description of the inner structure of discontinuous mentions. We rely on a weighted finite state automaton for both marginal and maximum a posteriori inference. As such, our method is sound in the sense that (1) well-formedness of predicted tag sequences is ensured via the automaton structure and (2) there is an unambiguous mapping between well-formed sequences of tags and (discontinuous) mentions. We evaluate our approach on three English datasets in the biomedical domain, and report comparable results to state-of-the-art while having a way simpler and faster model.
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