Abstract
Domain term extraction plays an important role in natural language processing. Term extraction of the Belt and Road Initiative (BRI) texts based on BERT model is helpful to follow the trend in this field and appreciate the value of “The Belt and Road Initiative” development. It is an inevitable trend of technological development to use natural language processing technology to term extraction. In order to improve the efficiency of domain term extraction, a term extraction model based on BERT-BiLSTM-CRF (Bidirectional Long Short-Term Memory-Conditional Random Field) is applied to obtain annotation sequence of the BRI terms after input, then the extracted term results are analyzed. Compared with other term extraction models, the BRI term extraction model based on BERT-BiLSTM-CRF achieved good results in terms of precision, recall and F1-score, showing that the model is feasible and effective.
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