Assembly Calculus (AC), proposed by Papadimitriou et al., aims to reproduce advanced cognitive functions through simulating neural activities, with several applications based on AC having been developed, including a natural language parser proposed by Mitropolsky et al. However, this parser lacks the ability to handle Kleene closures, preventing it from parsing all regular languages and rendering it weaker than Finite Automata (FA). In this paper, we propose a new bionic natural language parser (BNLP) based on AC and integrates two new biologically rational structures, Recurrent Circuit and Stack Circuit which are inspired by RNN and short-term memory mechanism. In contrast to the original parser, the BNLP can fully handle all regular languages and Dyck languages. Therefore, leveraging the Chomsky-Sch \H{u}tzenberger theorem, the BNLP which can parse all Context-Free Languages can be constructed. We also formally prove that for any PDA, a Parser Automaton corresponding to BNLP can always be formed, ensuring that BNLP has a description ability equal to that of PDA and addressing the deficiencies of the original parser.

翻译：由Papadimitriou等人提出的组装演算（Assembly Calculus, AC）旨在通过模拟神经活动重现高级认知功能，目前已开发出多项基于AC的应用，包括Mitropolsky等人提出的自然语言解析器。然而，该解析器无法处理Kleene闭包，导致其不能解析所有正则语言，能力弱于有限自动机（Finite Automata, FA）。本文提出一种基于AC的新型仿生自然语言解析器（Bionic Natural Language Parser, BNLP），该解析器融合了受RNN和短时记忆机制启发的两种新颖生物合理结构——循环电路（Recurrent Circuit）和堆栈电路（Stack Circuit）。与原解析器相比，BNLP能够完整处理所有正则语言和Dyck语言。进而，利用Chomsky-Schützenberger定理，可构建出能解析所有上下文无关语言（Context-Free Languages）的BNLP。我们还从形式上证明：对于任意下推自动机（PDA），总能构造出对应BNLP的解析自动机（Parser Automaton），从而确保BNLP具有与PDA等价的描述能力，弥补了原解析器的不足。