Semantic Ambiguity in Japanese Architectural Terms: A Computational Contextual Analysis

Japanese architectural terms are often translated into English as if they named stable spatial objects, yet many of these terms carry material, relational, symbolic, and experiential meanings. This study investigates contextual variation relevant to possible semantic ambiguity in English-language discussions of eight Japanese architectural terms: engawa, tokonoma, ma, mu, shakkei, doma, shōji, and fusuma. It uses an audited contextual-semantic workflow based on a fine-tuned multilingual BERT masked-language model and two paired corpus conditions. FULL preserves broader explanatory and glossary-like contexts, while CLEAN removes explicitly definitional sentences to test whether contextual patterns persist after definitional support is reduced. The analysis combines target screening, sentence-level contextual clustering, architectural register alignment, cluster-aware interpretation, and a focused concept-alignment test for engawa. The results show uneven evidence of recurring contextual differentiation across the target set; the clusters do not independently establish distinct lexical senses. Engawa, tokonoma, ma, shakkei, and shōji show the most stable contextual structure, although the semantic significance of their clusters varies; mu functions mainly as a conceptual comparison case, fusuma supports a cautious partition-and-recomposition reading, and doma remains a low-stability diagnostic case. For engawa, positive similarity to preselected boundary-edge and connector-flow prompts is compatible with a combined architectural reading, developed here as the modal littoral-circuit space formulation. Overall, the study shows that contextual modeling can make ambiguity in Japanese architectural language more explicit and testable without reducing these terms to fixed semantic categories.