  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.
