因中文字結構複雜、同音字多與字形相似等特性，中文拼寫檢查（Chinese Spelling Check, CSC）面臨諸多挑戰，使得錯字偵測高度依賴語境理解。本研究提出一個新穎的 CSC 框架—Summarization-Enhanced BERT（SE-BERT），結合句子摘要特徵、字音資訊與字形嵌入，以提升模型在錯誤偵測與糾正任務中的語意感知能力。該模型由摘要模組、偵測網路與糾正網路三部分組成，並加入錯誤導向遮罩機制，提供更具針對性的修正指引。在 SIGHAN 標準資料集上進行實驗後顯示，SE-BERT 在準確率與錯誤識別能力方面皆優於現有基準模型，且能有效降低過度修正的情形。注意力視覺化與個案分析亦驗證模型能聚焦於語意關鍵位置。整體而言，本研究證實整合語意、語音與視覺資訊對於提升中文拼寫校正成效的重要性，並提供一個具結構性且可擴展的拼字校正解決方案，適用於語言特性多變的應用場景。;Due to the structural complexity of Chinese characters, the high occurrence of homophones, and visual similarity among glyphs, Chinese spelling check (CSC) presents unique challenges. These factors make typo detection highly context-dependent. This study proposes a novel CSC framework, Summarization-Enhanced BERT (SE-BERT), which integrates phonetic and glyph embeddings with sentence-level summarization features to enhance context awareness in error detection and correction. The model consists of a summarization module, a detection network, and a correction network, augmented with an error-guided mask that guides more precise correction. Experiments conducted on benchmark datasets, including SIGHAN, demonstrate that SE-BERT achieves superior performance compared to existing baselines, particularly in reducing miscorrections and improving accuracy. Attention visualization and case studies further confirm the model′s ability to focus on key contextual cues. These findings highlight the importance of multi-source information integration like semantic, phonetic, and visual, for effective CSC, offering a structured and adaptable approach for spelling correction in linguistically complex environments.
