Error detection and correction are essential for ensuring robust and reliable operation in modern communication systems, particularly in complex transmission environments. However, discussions on these topics have largely been overlooked in semantic communication (SemCom), which focuses on transmitting meaning rather than symbols, leading to significant improvements in communication efficiency. Despite these advantages, semantic errors -- stemming from discrepancies between transmitted and received meanings -- present a major challenge to system reliability. This paper addresses this gap by proposing a comprehensive framework for detecting and correcting semantic errors in SemCom systems. We formally define semantic error, detection, and correction mechanisms, and identify key sources of semantic errors. To address these challenges, we develop a Gaussian process (GP)-based method for latent space monitoring to detect errors, alongside a human-in-the-loop reinforcement learning (HITL-RL) approach to optimize semantic model configurations using user feedback. Experimental results validate the effectiveness of the proposed methods in mitigating semantic errors under various conditions, including adversarial attacks, input feature changes, physical channel variations, and user preference shifts. This work lays the foundation for more reliable and adaptive SemCom systems with robust semantic error management techniques.

翻译：错误检测与校正确保现代通信系统在复杂传输环境下鲁棒可靠运行的关键机制。然而，在专注于传输语义而非符号、显著提升通信效率的语义通信领域，相关讨论长期被忽视。尽管语义通信具有诸多优势，由发送与接收语义间差异引发的语义错误，仍对系统可靠性构成重大挑战。本文通过提出语义通信系统中语义错误检测与校正的完整框架来填补这一空白。我们正式定义了语义错误、检测机制及校正机制，并系统识别了语义错误的主要来源。针对这些挑战，我们开发了基于高斯过程的潜在空间监控方法用于错误检测，同时提出人机协同强化学习方法，利用用户反馈优化语义模型配置。实验结果表明，所提方法在对抗攻击、输入特征变化、物理信道波动及用户偏好迁移等多种条件下，均能有效缓解语义错误。本研究为构建具备鲁棒语义错误管理能力的可靠自适应语义通信系统奠定了理论基础。