A deep learning architecture for semantic address matching

References

• Bergstra, J. and Bengio, Y., 2012. Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13, 281–305.
   Web of Science ®Google Scholar
• Beyer, K., et al., 2019. Building capacity for geospatial cancer research in Uganda: a feasibility study. The Lancet Global Health, 7, S11. doi:10.1016/S2214-109X(19)30096-8
   Web of Science ®Google Scholar
• Breiman, L., 2001. Random forests. Machine Learning, 45 (1), 5–32. doi:10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Breuel, T.M., 2015. The effects of hyperparameters on SGD training of neural networks. arXiv Preprint arXiv:1508.02788.
   Google Scholar
• Chang, C.C. and Lin, C.J., 2011. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2 (3), 27.
   Web of Science ®Google Scholar
• Charif, O., et al. 2010. A method and a tool for geocoding and record linkage. In: 2010 Second IITA International Conference on Geoscience and Remote Sensing. Qingdao, China: IEEE, 356–359.
   Google Scholar
• Chen, Q., et al., 2016. Enhanced LSTM for natural language inference. arXiv Preprint arXiv:1609.06038.
   Google Scholar
• Comber, S. and Arribas-Bel, D., 2019. Machine learning innovations in address matching: A practical comparison of word2vec and CRFs. Transactions in GIS, 23 (2), 334–348. doi:10.1111/tgis.12522.
   Web of Science ®Google Scholar
• Ding, R. and Chen, Z., 2018. RecNet: A deep neural network for personalized POI recommendation in location-based social networks. International Journal of Geographical Information Science, 32 (8), 1631–1648. doi:10.1080/13658816.2018.1447671.
   Web of Science ®Google Scholar
• Edwards, S.E., Strauss, B., and Miranda, M.L., 2014. Geocoding large population-level administrative datasets at highly resolved spatial scales. Transactions in GIS, 18 (4), 586–603. doi:10.1111/tgis.2014.18.issue-4.
   PubMed Web of Science ®Google Scholar
• Fan, Y., et al., 2017. MatchZoo: A toolkit for deep text matching. arXiv Preprint arXiv:1707.07270.
   Google Scholar
• Gibbs, J.W., 2010. Elementary principles in statistical mechanics: developed with especial reference to the rational foundation of thermodynamics (Cambridge library collection - mathematics). Cambridge: Cambridge University Press.
   Google Scholar
• Goldberg, W.D., Wilson, J.P., and Knoblock, C.A., 2007. From text to geographic coordinates: the current state of geocoding. Journal of the Urban and Regional Information Systems Association, 19 (1), 33–46.
   Google Scholar
• Grekousis, G., 2019. Artificial neural networks and deep learning in urban geography: A systematic review and meta-analysis. Computers, Environment and Urban Systems, 74, 244–256. doi:10.1016/j.compenvurbsys.2018.10.008
   Web of Science ®Google Scholar
• Han, S., et al., 2016. THUOCL: tsinghua Open Chinese Lexicon [Online]. Available from: https://github.com/thunlp/THUOCL (Accessed: 24 March 2018)
   Google Scholar
• He, J., et al., 2018. Mining transition rules of cellular automata for simulating urban expansion by using the deep learning techniques. International Journal of Geographical Information Science, 32 (10), 2076–2097. doi:10.1080/13658816.2018.1480783.
   Web of Science ®Google Scholar
• Hochreiter, S. and Schmidhuber, J., 1997. Long short-term memory. Neural Computation, 9 (8), 1735–1780. doi:10.1162/neco.1997.9.8.1735.
   PubMed Web of Science ®Google Scholar
• Jaccard, P., 1908. Nouvelles Recherches Sur la Distribution Florale. Bulletin De La Société Vaudoise Des Sciences Naturelles, 44, 223–270.
   Google Scholar
• Jaro, M.A., 1989. Advances in record-linkage methodology as applied to matching the 1985 census of Tampa, Florida. Journal of the American Statistical Association, 84 (406), 414–420. doi:10.1080/01621459.1989.10478785.
   Web of Science ®Google Scholar
• Jiang, B. and Yao, X., 2006. Location-based services and GIS in perspective. Computers, Environment and Urban Systems, 30 (6), 712–725. doi:10.1016/j.compenvurbsys.2006.02.003.
   Web of Science ®Google Scholar
• Kang, M., Du, Q., and Wang, M., 2015. A new method of Chinese address extraction based on address tree model. Acta Geodaetica Et Cartographica Sinic, 44 (1), 99–107.
   Google Scholar
• Karimzadeh, M., et al., 2019. GeoTxt: A scalable geoparsing system for unstructured text geolocation. Transactions in GIS, 23 (1), 118–136. doi:10.1111/tgis.v23.1.
   Web of Science ®Google Scholar
• Kingma, D. and Ba, J., 2015. Adam: a method for stochastic optimization. In: International Conference for Learning Representations (ICLR). San Diego, CA.
   Google Scholar
• Lauritzen, S., 2006. Graphical Models. New York: Clarendon Press.
   Google Scholar
• Le, Q. and Mikolov, T., 2014. Distributed representations of sentences and documents. In: International Conference on International Conference on Machine Learning (ICML). Beijing, China.
   Google Scholar
• Levenshtein, V., 1966. Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady, 10, 707–710.
   Google Scholar
• Levy, O., Goldberg, Y., and Dagan, I., 2018. Improving distributional similarity with lessons learned from word embeddings. Transactions of the Association for Computational Linguistics, 3, 211–225. doi:10.1162/tacl_a_00134
   Google Scholar
• Li, L., et al., 2018. A hybrid method for Chinese address segmentation. International Journal of Geographical Information Science, 32 (1), 30–48. doi:10.1080/13658816.2017.1379084.
   Web of Science ®Google Scholar
• Li, W. and Hsu, C.Y., 2018. Automated terrain feature identification from remote sensing imagery: a deep learning approach. International Journal of Geographical Information Science, 5 (2–3), 115–141.
   Google Scholar
• Melo, F. and Martins, B., 2017. Automated geocoding of textual documents: a survey of current approaches. Transactions in GIS, 21 (1), 3–38. doi:10.1111/tgis.2017.21.issue-1.
   Web of Science ®Google Scholar
• Mikolov, T., et al., 2013a. Efficient estimation of word representations in vector space. In: International Conference on Learning Representations (ICLR). Scottsdale, Arizona.
   Google Scholar
• Mikolov, T., et al., 2013b. Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems, 26, 3111–3119.
   Google Scholar
• Murrieta-Flores, P. and Martins, B., 2018. Learning to combine multiple string similarity metrics for effective toponym matching. International Journal of Digital Earth, 11 (9), 913–938. doi:10.1080/17538947.2017.1371253.
   Web of Science ®Google Scholar
• Ouma, P.O., et al., 2018. Access to emergency hospital care provided by the public sector in sub-Saharan Africa in 2015: a geocoded inventory and spatial analysis. The Lancet Global Health, 6 (3), e342–e350. doi:10.1016/S2214-109X(17)30488-6.
   PubMed Web of Science ®Google Scholar
• Parikh, P.A., et al., 2016. A decomposable attention model for natural language inference. In: Conference on Empirical Methods in Natural Language Processing (EMNLP). Austin, Texas.
   Google Scholar
• Powers, D.M.W., 2011. Evaluation: from precision, recall and f-measure to roc., informedness, markedness & correlation. Journal of Machine Learning Technologies, 2 (1), 37–63.
   Google Scholar
• Řehůřek, R. and Sojka, P., 2010. Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks. Valletta, Malta: University of Malta, 45–50.
   Google Scholar
• Santos, R., et al., 2018. Toponym matching through deep neural networks. International Journal of Geographical Information Science, 32 (2), 324–348. doi:10.1080/13658816.2017.1390119.
   Web of Science ®Google Scholar
• Srivastava, S., et al., 2018. Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data. International Journal of Geographical Information Science. doi:10.1080/13658816.2018.1542698.
   Google Scholar
• Sun, Z., et al., 2013. Technology of fuzzy Chinese-geocoding method. In: International Conference on Information Science and Cloud Computing (ISCC), Guangzhou, China.
   Google Scholar
• Tian, Q., et al., 2016. Using an optimized Chinese address matching method to develop a geocoding service: a case study of Shenzhen, China. ISPRS International Journal of Geo-Information, 5 (5), 65. doi:10.3390/ijgi5050065.
   Web of Science ®Google Scholar
• Wallach, H.M., 2004. Conditional random fields: an introduction. Philadelphia, PA: University of Pennsylvania Department of Computer and Information Science Technical Report No. MS-CIS-04-21.
   Google Scholar
• Yao, Y., et al., 2017. Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model. International Journal of Geographical Information Science, 31 (4), 825–848. doi:10.1080/13658816.2016.1244608.
   Web of Science ®Google Scholar
• Yu, M. and Dredze, M., 2014. Improving lexical embeddings with semantic knowledge. In: Annual Meeting of the Association for Computational Linguistics (ACL), Baltimore, Maryland.
   Google Scholar