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This paper describes DM-NLP's system for toponym resolution task at Semeval 2019. Our system was developed for toponym detection, disambiguation and end-to-end resolution which is a pipeline of the former two. For toponym detection, we utilized the stateof-the-art sequence labeling model, namely, BiLSTM-CRF model as backbone. A lot of strategies are adopted for further improvement, such as pre-training, model ensemble, model averaging and data augment. For toponym disambiguation, we adopted thedoi:10.18653/v1/s19-2156 dblp:conf/semeval/WangMZLXLS19 fatcat:v35fcolkzjh2fhic66i2pi7wia