Named Entity Recognition in Geological Field Based on BERT-BiLSTM-CRF
Recognition of geological entities based on geological texts plays a major role in mining and analyzing data for geo-logical researchers.This technology is also the basis for building knowledge graph in the geological field and lots of upper-level ap-plications.At present,the research of entity recognition in the field of geology is still under development,with fewer applications,but the amount of geological data is increasing exponentially,so data processing technology is particularly important.Therefore this paper proposes a named entity recognition technology based on the BERT-BiLSTM-CRF model and constraint rules to assist geologi-cal professionals in processing geological data.First,the BERT layer receives the input text sequences,and converts them into word vectors with contextual features.Next the word vectors are input into the BiLSTM layer to learn the contextual features,and the BiL-STM layer outputs the scores of every single chinese character.After this,the CRF layer integrates the scores from the BiLSTM layer and the implicit rules which are learned by itself,then the final comprehensive scores are output so as to select the best label.The experimental results show that compared with the traditional method and the popular deep learning method,the precision,recall,and F1 value of this method are all the highest values,which are 92.05%,94.82%,and 93.41%,respectively.
named entity recognitionknowledge graphdeep learninggeological fieldBERT
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