Named entity recognition (NER) is an essential task in natural language
processing, but the internal mechanism of most NER models is a black box for
users. In some high-stake decision-making areas, improving the interpretability
of an NER method is crucial but challenging. In this paper, based on the
existing Deterministic Talmudic Public announcement logic (TPK) model, we
propose a novel binary tree model (called BTPK) and apply it to two widely used
Bi-RNNs to obtain BTPK-based interpretable ones. Then, we design a
counterfactual verification module to verify the BTPK-based learning method.
Experimental results on three public datasets show that the BTPK-based learning
outperform two classical Bi-RNNs with self-attention, especially on small,
simple data and relatively large, complex data. Moreover, the counterfactual
verification demonstrates that the explanations provided by the BTPK-based
learning method are reasonable and accurate in NER tasks. Besides, the logical
reasoning based on BTPK shows how Bi-RNNs handle NER tasks, with different
distance of public announcements on long and complex sequences.

本文提出了一种基于二叉树模型的可解释的 NER 学习方法，并通过反事实验证模块验证了其合理性和准确性。通过实验，在三个公共数据集上证明了该方法的优越性，尤其是在小规模简单数据和相对较大复杂数据上。此外，基于 BTPK 的逻辑推理显示出 Bi-RNNs 如何处理 NER 任务。