地理语义相关性和相似性的评估基线
An evaluative baseline for geo-semantic relatedness and similarity
摘要：
在地理信息科学和语义学中，语义相似度的计算被广泛认为是支持大量信息集成和检索任务的关键。相比之下，地理语义相关性的作用在很大程度上被忽视了。在自然语言处理中，语义相关性经常与更具体的语义相似性混淆。在这篇文章中，我们讨论了一个概念的地理语义相关性的基础上莱勒的语义场，我们比较它与地理语义相似性。然后，我们描述和验证地理相关性和相似性数据集（GeReSiD），一个新的开放数据集，旨在评估地理语义相关性和相似性的计算措施。该数据集比现有的此类数据集更大，包括97个地理术语，由203名人类受试者组成50个术语对。GeReSiD可在线获得，并可用作评估基线，以凭经验确定给定计算模型在多大程度上近似地理语义相关性和相似性。
英文摘要：
In geographic information science and semantics, the computation of semantic similarity is widely recognised as key to supporting a vast number of tasks in information integration and retrieval. By contrast, the role of geo-semantic relatedness has been largely ignored. In natural language processing, semantic relatedness is often confused with the more specific semantic similarity. In this article, we discuss a notion of geo-semantic relatedness based on Lehrer's semantic fields, and we compare it with geo-semantic similarity. We then describe and validate the Geo Relatedness and Similarity Dataset (GeReSiD), a new open dataset designed to evaluate computational measures of geo-semantic relatedness and similarity. This dataset is larger than existing datasets of this kind, and includes 97 geographic terms combined into 50 term pairs rated by 203 human subjects. GeReSiD is available online and can be used as an evaluation baseline to determine empirically to what degree a given computational model approximates geo-semantic relatedness and similarity.
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。
