Natural language understanding is one of the most challenging topics in artificial intelligence. Deep neural network methods, particularly large language module (LLM) methods such as ChatGPT and GPT-3, have powerful flexibility to adopt informal text but are weak on logical deduction and suffer from the out-of-vocabulary (OOV) problem. On the other hand, rule-based methods such as Mathematica, Semantic web, and Lean, are excellent in reasoning but cannot handle the complex and changeable informal text. Inspired by pragmatics and structuralism, we propose two strategies to solve the OOV problem and a semantic model for better natural language understanding and reasoning.

翻译：自然语言理解是人工智能领域最具挑战性的课题之一。深度神经网络方法，尤其是ChatGPT、GPT-3等大型语言模型方法，在适应非正式文本方面展现出强大的灵活性，但在逻辑推理方面存在不足，且面临词汇外问题。另一方面，基于规则的方法，如Mathematica、语义网和Lean，在推理方面表现优异，但无法处理复杂多变的不规范文本。受语用学与结构主义的启发，我们提出两种策略以解决词汇外问题，并构建了一个语义模型，旨在实现更好的自然语言理解与推理能力。