Spatial Context-Based Local Toponym Extraction and Chinese Textual Address Segmentation from Urban POI Data
Spatial Context-Based Local Toponym Extraction and Chinese Textual Address Segmentation from Urban POI Data
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基于空间上下文的城市 POI 数据中的本地地名提取和中文文本地址分割
DOI：
10.3390/ijgi9030147
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发表时间：
2020-03
影响因子：
3.4
通讯作者：
Guo Han
中科院分区：
文献类型：
--
作者：
Kuai Xi;Guo Renzhong;Zhang Zhijun;He Biao;Zhao Zhigang;Guo Han
通过地名（即地名词）进行地理参照，是将文本信息与地理位置相关联的最常见方式。计算机使用数字坐标（如经纬度对）来表示地点，而人们通常提及……
（注：原文最后“people generally refer t”表述不完整，推测可能是“refer to” ，以上翻译按推测补充完整后进行。）
Georeferencing by place names (known as toponyms) is the most common way of associating textual information with geographic locations. While computers use numeric coordinates (such as longitude-latitude pairs) to represent places, people generally refer to places via their toponyms. Query by toponym is an effective way to find information about a geographic area. However, segmenting and parsing textual addresses to extract local toponyms is a difficult task in the geocoding field, especially in China. In this paper, a local spatial context-based framework is proposed to extract local toponyms and segment Chinese textual addresses. We collect urban points of interest (POIs) as an input data source; in this dataset, the textual address and geospatial position coordinates correspond at a one-to-one basis and can be easily used to explore the spatial distribution of local toponyms. The proposed framework involves two steps: address element identification and local toponym extraction. The first step identifies as many address element candidates as possible from a continuous string of textual addresses for each urban POI. The second step focuses on merging neighboring candidate pairs into local toponyms. A series of experiments are conducted to determine the thresholds for local toponym extraction based on precision-recall curves. Finally, we evaluate our framework by comparing its performance with three well-known Chinese word segmentation models. The comparative experimental results demonstrate that our framework achieves a better performance than do other models.
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10.1080/13658816.2018.1458989
发表时间：
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影响因子：
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影响因子：
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期刊：
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