DeepAM: Deep semantic address representation for address matching
dc.contributor.author | Shan, Shuangli | |
dc.contributor.author | Li, Zhixu | |
dc.contributor.author | Yang, Qiang | |
dc.contributor.author | Liu, An | |
dc.contributor.author | Xu, Jiajie | |
dc.contributor.author | Chen, Zhigang | |
dc.date.accessioned | 2019-10-02T11:56:22Z | |
dc.date.available | 2019-10-02T11:56:22Z | |
dc.date.issued | 2019-07-18 | |
dc.identifier.citation | Shan, 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 | |
dc.identifier.doi | 10.1007/978-3-030-26072-9_4 | |
dc.identifier.uri | http://hdl.handle.net/10754/656842 | |
dc.description.abstract | 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. | |
dc.description.sponsorship | This 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). | |
dc.publisher | Springer Nature | |
dc.relation.url | http://link.springer.com/10.1007/978-3-030-26072-9_4 | |
dc.rights | Archived with thanks to Springer International Publishing | |
dc.title | DeepAM: Deep semantic address representation for address matching | |
dc.type | Conference Paper | |
dc.contributor.department | King Abdullah University of Science and Technology, Jeddah, Saudi Arabia | |
dc.conference.date | 2019-08-01 to 2019-08-03 | |
dc.conference.name | 3rd APWeb and WAIM Joint Conference on Web and Big Data, APWeb-WAIM 2019 | |
dc.conference.location | Chengdu, CHN | |
dc.eprint.version | Post-print | |
dc.contributor.institution | Institute of Artificial Intelligence, School of Computer Science and Technology, Soochow University, Suzhou, China | |
dc.contributor.institution | Neusoft Corporation, Shenyang, China | |
dc.contributor.institution | IFLYTEK Research, Suzhou, China | |
dc.contributor.institution | State Key Laboratory of Cognitive Intelligence, iFLYTEK, Hefei, People's Republic of China | |
kaust.person | Yang, Qiang | |
refterms.dateFOA | 2019-10-03T12:46:13Z | |
dc.date.published-online | 2019-07-18 | |
dc.date.published-print | 2019 |