DeepAM: Deep semantic address representation for address matching
Online Publication Date2019-07-18
Print Publication Date2019
Permanent link to this recordhttp://hdl.handle.net/10754/656842
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AbstractAddress 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.
CitationShan, S., Li, Z., Qiang, Y., Liu, A., Xu, J., & Chen, Z. (2019). DeepAM: Deep Semantic Address Representation for Address Matching. Lecture Notes in Computer Science, 45–60. doi:10.1007/978-3-030-26072-9_4
SponsorsThis research is partially supported by National Natural Science Foundation of China (Grant No. 61632016, 61572336, 61572335, 61772356), the Natural Science Research Project of Jiangsu Higher Education Institution (No. 17KJA520003, 18KJA520010), and the Open Program of Neusoft Corporation (No. SKLSAOP1801).
PublisherSpringer International Publishing
Conference/Event name3rd APWeb and WAIM Joint Conference on Web and Big Data, APWeb-WAIM 2019