Spatial Semantic-based Enhanced Address Parsing via Adaptive Weighted Learning

作者：Huiling Qin, Ming Wang, Yuanxun Li, Junbo Zhang, Yu Zheng · 年份：2025 · DOI：10.1145/3746252.3761499 · 被引用次数：1 · 研究领域：Human Mobility and Location-Based Analysis、Data-Driven Disease Surveillance、Topic Modeling

Address parsing is an essential task that transforms natural language descriptions into standardized addresses, crucial for numerous urban applications. Existing methods struggle with ambiguous expressions, and even Large Language Models face challenges adapting to specialized domains with limited data. In this study, we focus on developing a robust framework to map diverse address descriptions into a unified semantic space of standardized addresses. We propose the Adaptive Weighted Learning-based Address Parsing (AWLAP) framework, which enhances parsing effectiveness through two key components: a multi-level constrained classifier that mines correlations between geographic entities across hierarchies, and an integrated discriminator that adaptively guides optimization based on parsing complexity. We evaluate the AWLAP using real data from JD Logistics and Point-of-Interest addresses. Extensive experiments comparing against state-of-the-art methods demonstrate AWLAP's effectiveness and robustness in address parsing. The proposed AWLAP framework has been successfully deployed as an address parsing service in practical applications.