engleski

Named entity recognition for addresses: an empirical study

In this paper, we develop a high-performing named entity recognition model for addresses which deals with challenges including diversity, ambiguity and complexity of the address entity. Different model architectures are used for training the classifier, including logistic regression and random forest models as well as the more complex bidirectional LSTM network with a conditional random field layer (BiLSTM-CRF) implemented using Flair framework. Experiments are conducted using variously configured models on two sets of corpora, tagged differently based on the granularity of address entity: entire address, and address consisting of subparts. For both corpora, the best results are achieved on a BiLSTM-CRF architecture model with a single RNN layer trained on either standalone BERT embeddings or a stacked combination of BERT and GloVe.

named entity recognition ; natural language processing ; address entity, bert ; bilstm-crf architecture ; flair, bilstm-cnn architecture

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