本研究提出一套整合深度學習與空間計算的地名消歧異框架。首先，利用客製化之命名實體識別模型（NER-MS）從明代段落中擷取地名；其次，鑑於明代地圖精度之不足，本研究採用1930年代河北省地圖作為地名資料來源；最後，建構基於行政邊界凸包（Convex Hull）與 5 公里緩衝區（Buffer）的空間過濾機制，有效降低同名異地之歧義問題。;Historical local records passages serve as invaluable repositories for understanding social mobility and settlement patterns. However, standard Historical Geographic Information System (HGIS) databases, such as the Chinese Civilization in Time and Space (CCTS), primarily index high-level administrative place names, lacking the granularity required to map village-level settlements. Furthermore, the prevalence of generic place names (e.g., ”Wang Village”) introduces significant spatial ambiguity, hindering auto mated localization efforts.
This study proposes an integrated framework combining Named Entity Recognition (NER) with computational geometry to address these challenges. The methodology proceeds in three stages: first, extracting toponyms using a custom-trained NER model (NER-MS); second, utilizing 1930s historical maps of Hebei (河北) as a comparative reference materials to bridge the gap between pre-modern texts and modern coordinates; and third, implementing a spatial filtering mechanism based on administrative convex hulls augmented with a 5 km buffer to resolve homonymy.
