Chinese Address Similarity Calculation Based on Auto Geological Level Tagging

Automatic Identification of Addresses: A Systematic Literature Review

“…The biggest contribution of the paper at hand, however, is the thorough documentation of all the steps required to execute the proposed model's workflow. In other papers included in the present literature review, CRFs are used as a benchmark model, e.g.,: Dani et al [55] or in combination with other methods, which will be further addressed [56][57][58].…”

“…Within the present literature review, several of the considered papers propose these types of methods, namely the ones by Santos et al [35], Lin et al [9], J. Liu et al [58], Shan et al [7,29], P. Li et al [69], and Chen et al [70]. To take into account contextual information both from previous and future tokens, by processing the sequence in two directions, bidirectional LSTM (BiLSTM) or GRU layers are also being employed in the great majority of these studies.…”

“…The best performing models further connect the encoder and decoder through an attention mechanism in order to assign higher weights to the most important features [7,9,29]. With a view to reduce overfitting and enhance the classification models' generalization abilities, a dropout regularization layer is also normally added [9,35,58]. The ESIM model [71] consists of an illustrative example of a deep learning architecture based on the principles previously described.…”

“…The deep neural network based on GRUs, to categorize toponym pairs as matches or non-matches, proposed by Santos et al [35], also outperforms traditional text-matching methods, achieving an increase of almost 10 points in most of the evaluation metrics (namely, accuracy, precision, and F1). The LM-LSTM-CRF+BP neural networks model proposed by J. Liu et al [58] achieves an accuracy and F1 score of 87%, compared with average scores of 70% by the benchmark methods (word2vec and edit distance). The address GCN method proposed by Shan et al [7] also presents better results, on both precision (up to 8%) and recall (up to 12%), than the existing methods, which include the DeepAM model previously proposed by the same author, based on an encoder-decoder architecture with two LSTM networks [29].…”

Deep Transfer Learning Model for Semantic Address Matching

“…Comber et al used CRF and word2vec for address matching to extract the semantics of addresses without designing complex rules [38]; Zhang et al provides a convolutional neural network (W-TextCNN) for Chinese address pattern classification [39]. With the popularity of gating mechanism neural networks, address matching and normalizing based on LSTM and GRU have been carried out by an increasing number of researchers [40][41][42][43]. Santos et al used a deep neural network based on bidirectional GRUs for place name matching [44]; Shan enriched the address context by collecting address data on the Internet and trained an address representation model with two LSTMs and attention mechanisms to extract address vectors [45].…”