标题: A deep learning architecture for semantic address matching
作者: Yue Lin, Mengjun Kang, Yuyang Wu, Qingyun Du, Tao Liu
来源出版物: International Journal of Geographical Information Science DOI: doi.org/10.1080/13658816.2019.1681431 出版年: 2019
摘要: Address matching is a crucial step in geocoding, which plays an important role in urban planning and management. To date, the unprecedented development of location-based services has generated a large amount of unstructured address data. Traditional address matching methods mainly focus on the literal similarity of address records and are therefore not applicable to the unstructured address data. In this study, we introduce an address matching method based on deep learning to identify the semantic similarity between address records. First, we train the word2vec model to transform the address records into their corresponding vector representations. Next, we apply the enhanced sequential inference model (ESIM), a deep text-matching model, to make local and global inferences to determine if two addresses match. To evaluate the accuracy of the proposed method, we fine-tune the model with real-world address data from the Shenzhen Address Database and compare the outputs with those of several popular address matching methods. The results indicate that the proposed method achieves a higher matching accuracy for unstructured address records, with its precision, recall, and F1 score (i.e., the harmonic mean of precision and recall) reaching 0.97 on the test set.
作者关键词: Geocoding, deep neural network, machine learning, semantic matching, word2vec
通讯作者：Mengjun Kang firstname.lastname@example.org
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