  Extracting precise geographical information from textual contents is crucial
in a plethora of applications. For example, during hazardous events, a robust
and unbiased toponym extraction framework can provide an avenue to tie the
location concerned to the topic discussed by news media posts and pinpoint
humanitarian help requests or damage reports from social media. Early studies
have leveraged rule-based, gazetteer-based, deep learning, and hybrid
approaches to address this problem. However, the performance of existing tools
is deficient in supporting operations like emergency rescue, which relies on
fine-grained, accurate geographic information. The emerging pretrained language
models can better capture the underlying characteristics of text information,
including place names, offering a promising pathway to optimize toponym
recognition to underpin practical applications. In this paper, TopoBERT, a
toponym recognition module based on a one dimensional Convolutional Neural
Network (CNN1D) and Bidirectional Encoder Representation from Transformers
(BERT), is proposed and fine-tuned. Three datasets (CoNLL2003-Train,
Wikipedia3000, WNUT2017) are leveraged to tune the hyperparameters, discover
the best training strategy, and train the model. Another two datasets
(CoNLL2003-Test and Harvey2017) are used to evaluate the performance. Three
distinguished classifiers, linear, multi-layer perceptron, and CNN1D, are
benchmarked to determine the optimal model architecture. TopoBERT achieves
state-of-the-art performance (f1-score=0.865) compared to the other five
baseline models and can be applied to diverse toponym recognition tasks without
additional training.
