通过聚类从描述中消除细粒度地名的歧义
Disambiguating fine-grained place names from descriptions by clustering
摘要：
日常的地名描述通常包含细粒度特征的地名，如建筑物或企业，这些地名比涉及较大地方的名称（如城市或自然地理特征）更难消除歧义。细粒度的地方往往更频繁，彼此之间更相似，而为较大的地方开发的消歧算法，例如基于人口或包含关系的消歧算法，通常不适用于这些情况。在这项研究中，我们解决了从日常的地方描述细粒度的地名的消歧。为了这个目的，我们评估现有的不同的基于聚类的方法的性能，因为聚类方法不需要更多的知识，除了模糊的地名的位置。我们认为，不仅是专门为地名消歧开发的方法，而且聚类算法开发的一般数据采掘，可能会被利用。我们比较了这些方法与一种新的算法，并表明，新算法优于其他算法的消歧精度和距离误差在几个测试数据集。
英文摘要：
Everyday place descriptions often contain place names of fine-grained features, such as buildings or businesses, that are more difficult to disambiguate than names referring to larger places, for example cities or natural geographic features. Fine-grained places are often significantly more frequent and more similar to each other, and disambiguation heuristics developed for larger places, such as those based on population or containment relationships, are often not applicable in these cases. In this research, we address the disambiguation of fine-grained place names from everyday place descriptions. For this purpose, we evaluate the performance of different existing clustering-based approaches, since clustering approaches require no more knowledge other than the locations of ambiguous place names. We consider not only approaches developed specifically for place name disambiguation, but also clustering algorithms developed for general data mining that could potentially be leveraged. We compare these methods with a novel algorithm, and show that the novel algorithm outperforms the other algorithms in terms of disambiguation precision and distance error over several tested datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
