基于塔木德公共宣告逻辑的BTPK可解释方法用于命名实体识别任务
BTPK-based interpretable method for NER tasks based on Talmudic Public Announcement Logic
AI总结：
本文提出基于塔木德公共宣告逻辑的BTPK可解释模型，用于揭示BRNN在命名实体识别任务中的内部决策逻辑和上下文语义信息。
摘要：
作为自然语言处理（NLP）中的基本任务之一，命名实体识别（NER）是NLP下游任务（如信息抽取、句法分析、机器翻译等）的重要基础工具。当前命名实体识别模型的内部运行逻辑对用户而言是黑盒，因此用户没有依据来判断哪个命名实体更有意义。因此，一个用户友好的可解释识别过程对许多人来说将非常有用。在本文中，我们提出了一种新颖的可解释方法BTPK（二元塔木德公共宣告逻辑模型），以帮助用户理解基于塔木德公共宣告逻辑的命名实体识别任务的内部识别逻辑。BTPK模型还能够捕获输入句子中的语义信息，即句子的上下文依赖性。我们观察到BTPK的公共宣告呈现了BRNN的内部决策逻辑，并且从BTPK模型获得的解释向我们展示了BRNN本质上如何处理NER任务。
英文摘要：
As one of the basic tasks in natural language processing (NLP), named entity recognition (NER) is an important basic tool for downstream tasks of NLP, such as information extraction, syntactic analysis, machine translation and so on. The internal operation logic of current name entity recognition model is black-box to the user, so the user has no basis to determine which name entity makes more sense. Therefore, a user-friendly explainable recognition process would be very useful for many people. In this paper, we propose a novel interpretable method, BTPK (Binary Talmudic Public Announcement Logic model), to help users understand the internal recognition logic of the name entity recognition tasks based on Talmudic Public Announcement Logic. BTPK model can also capture the semantic information in the input sentences, that is, the context dependency of the sentence. We observed the public announcement of BTPK presents the inner decision logic of BRNNs, and the explanations obtained from a BTPK model show us how BRNNs essentially handle NER tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
