Abstract
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 űtzenberger 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.
Abstract (translated)
翻译： Assembly Calculus（AC）是由Papadimitriou等人提出的一种方法，旨在通过模拟神经活动来复制高级认知功能，基于AC已经开发了许多应用，包括Mitropolsky等人提出的自然语言解析器。然而，这个解析器缺乏处理Kleene闭合的能力，导致它无法解析所有正则语言，变得比有限自动机（FA）更弱。在本文中，我们提出了一个基于AC的新生物自然语言解析器（BNLP），并整合了两个新的生物合理结构：循环电路和堆栈电路，这些结构受到RNN和短期记忆机制的启发。与原始解析器不同，BNLP可以完全处理所有正则语言和Dyck语言。因此，利用Chomsky-Sch 税务总局论，可以构建出可以解析所有上下文无关语言的Parser自动机。我们还正式证明了，对于任何PDA，都可以形成一个与BNLP相应的Parser自动机，从而确保BNLP具有与PDA相同的描述能力，并解决了原解析器的不足之处。
URL
https://arxiv.org/abs/2404.17343
