用标本记录中的地名信息，精准定位当前地图上找不到的历史地名。
Georeferencing Non-Gazetteered Place Names using Biological Specimen Records
- 通过重复出现的地名和空间关系，反推未知地名的位置约束。
- 概率模型在伪地名基准上误差仅1.43公里，准确率36%。
- 适合研究历史地理、生物多样性或需要高精度定位的学者。
自然史机构收集的生物标本记录蕴含丰富的时空地理信息，反映了不同时期区域景观的生物多样性。本文利用新西兰Allan植物标本馆的数字化数据，识别出标本采集地描述中未被当前地名录收录的地点名称（非地名录地名，NGPs）。这些名称多为历史、方言或口语化地名，曾作为采集时的地标。研究聚焦于仅凭标本记录中的有限信息，对NGPs进行地理定位。为此，利用同一地名在不同标本记录中多次出现，并结合空间关系术语，提取并反转这些关系以生成对NGP位置的约束。该方法被应用于确定性、概率性和基于大语言模型（LLM）的方法中，实现文本空间推理能力的对比分析。在伪地名基准测试中，概率推理达到最高精度（中位误差1.43公里；1公里内准确率36%），而大语言模型虽表现良好但精度较低（中位误差1.80公里；1公里内准确率31%），表明尽管大模型发展迅速，但在高精度要求下传统建模仍具优势。
原文摘要 · Abstract (English)
Biological specimen records collected by natural history institutions constitute a rich source of temporal geographic knowledge, capturing biodiversity information about regional landscapes as they were recorded at different times. Using digitised data from the Allan Herbarium (New Zealand), this study identifies place names in these specimen locality descriptions that are absent from current gazetteers; we refer to these as non-gazetteer place names (NGPs). These place names are typically historical, vernacular, or colloquial and were used as landmarks to describe a specimen's location at the time of collection. We then investigate the problem of georeferencing the NGPs using only the limited information available in the specimen records. To resolve this, we leverage repeated occurrences of the same place name across specimen records with different specimen locations and spatial relation terms, extracting and inverting these relations to derive constraints on NGP locations. This approach is instantiated within deterministic, probabilistic, and LLM-based methods, enabling a comparative analysis of their strengths and limitations for text-based spatial inference. On a pseudo-NGP benchmark, probabilistic inference achieves the highest accuracy (median error 1.43 km; A@1 km 36%), while the LLM yields competitive but less precise estimates (median error 1.80 km; A@1 km 31%), indicating that, despite advances in LLMs, traditional modelling remains advantageous when high spatial precision is required.
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