Using an Optimized Chinese Address Matching Method to Develop a Geocoding Service: A Case Study of Shenzhen, China

Qin, Tian; Ren, Fei; Hu, Tao; Liu, Jiangtao; Li, Ruichang; Du, Qingyun

doi:10.3390/ijgi5050065

Cited by 45 publications

(31 citation statements)

References 27 publications

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“…Despite availability of commercial and no-cost geocoding strategies ( Goldstein et al, 2014 ; Faure et al, 2017 ), the automated geocoding of textual documents faces challenges, especially for development of language modeling methods for textual document geocoding ( Faure et al, 2017 ). The complexity of the address string format in Chinese text-based geocoding compounds these challenges ( Tian et al, 2016 ).…”

Section: Discussionmentioning

confidence: 99%

“…Because of its great importance, many geocoding methods have been developed including online services, commercial in-house services, as well as no-cost strategies using R ( Goldstein et al, 2014 ; Faure et al, 2017 ). However, Chinese geocoding faces great challenges due to the complexity of the address string format in Chinese, which contains no delimiters between Chinese words, and limited address reference resources ( Tian et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Lime Tolerance in Rhododendron Based on Herbarium Specimen and Geochemical Data

et al. 2018

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Rhododendrons are typically known to be calcifuges that cannot grow well in lime soils. Data on lime tolerance of different taxa in Rhododendron are scarce. Habitats of naturally distributed specimens of genus Rhododendron were compiled as Chinese text-based locations from the Chinese Virtual Herbarium. The locations were then geocoded into latitude/longitude pairs and subsequently connected to soil characteristics including pH and CaCO3 from the Harmonized World Soil Database (HWSD). Using the upper quartile values of pH > 7.2 and CaCO3 > 2% weight in topsoil as threshold, we predicted the lime tolerant taxa. A dataset of 31,146 Rhododendron specimens including the information on taxonomy, GPS locations and soil parameters for both top- and subsoil was built. The majority of the specimens were distributed in soils with moderately acidic pH and without presence of CaCO3. 76 taxa with potential lime tolerance were predicted out of 525 taxa. The large scale data analysis based on combined data of geocoded herbarium specimens and HWSD allows identification of valuable Rhododendron species, subspecies or botanical varieties with potential tolerance to lime soils with higher pH. The predicted tolerant taxa are valuable resources for an in-depth evaluation of lime tolerance or for further use in horticulture and breeding.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Lime Tolerance in Rhododendron Based on Herbarium Specimen and Geochemical Data

et al. 2018

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“…In rule-based approaches, first, a predefined address model consisting of address model features of different types and the relationships between them is established. Then, the string of textual address is segmented by a maximum matching procedure [15][16][17][18]. In statistics-based approaches, pre-statistical variants, such as character frequency or cooccurrence probability, and NLP models, such as the N-gram model, hidden Markov model (HMM), conditional random field (CRF) or branch entropy, are used to segment the string of text into multiple words [19][20][21][22].…”

Section: Related Workmentioning

confidence: 99%

Spatial Context-Based Local Toponym Extraction and Chinese Textual Address Segmentation from Urban POI Data

Kuai

Guo

Zhang

et al. 2020

IJGI

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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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“…We illustrate this compound framework in Fig. 1: Given a set of addresses, we first generate a number of candidate address pairs for matching based on some simple heuristic rules as introduced in [19]. For instance, only addresses in the same city and district (if any), sharing at least one word (after removing stop words) in the left part of their address strings need to be compared.…”

Section: A Compound Framework For Address Matchingmentioning

confidence: 99%

DeepAM: Deep Semantic Address Representation for Address Matching

Shan

Yang

et al. 2019

Lecture Notes in Computer Science

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Address matching is a crucial task in various location-based businesses like take-out services and express delivery, which aims at identifying addresses referring to the same location in address databases. It is a challenging one due to various possible ways to express the address of a location, especially in Chinese. Traditional address matching approaches relying on string similarities and learning matching rules to identify addresses referring to the same location, could hardly solve the cases with redundant, incomplete or unusual expression of addresses. In this paper, we propose to map every address into a fixed-size vector in the same vector space using state-of-the-art deep sentence representation techniques and then measure the semantic similarity between addresses in this vector space. The attention mechanism is also applied to the model to highlight important features of addresses in their semantic representations. Last but not least, we novelly propose to get rich contexts for addresses from the web through web search engines, which could strongly enrich the semantic meaning of addresses that could be learned. Our empirical study conducted on two real-world address datasets demonstrates that our approach greatly improves both precision (up to 5%) and recall (up to 8%) of the state-of-the-art existing methods.

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Using an Optimized Chinese Address Matching Method to Develop a Geocoding Service: A Case Study of Shenzhen, China

Cited by 45 publications

References 27 publications

Prediction of Lime Tolerance in Rhododendron Based on Herbarium Specimen and Geochemical Data

Prediction of Lime Tolerance in Rhododendron Based on Herbarium Specimen and Geochemical Data

Spatial Context-Based Local Toponym Extraction and Chinese Textual Address Segmentation from Urban POI Data

DeepAM: Deep Semantic Address Representation for Address Matching

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