Resumen
Shipping Enterprise Credit Named Entity Recognition (NER) aims to recognize shipping enterprise credit entities from unstructured shipping enterprise credit texts. Aiming at the problem of low entity recognition rate caused by complex and diverse entities and nesting phenomenon in the field of shipping enterprise credit, a deep learning method based on multi-feature fusion is proposed to improve the recognition effect of shipping enterprise credit entities. In this study, the shipping enterprise credit dataset is manually labeled using the BIO labeling model, combining the pre-trained model Bidirectional Encoder Representations from Transformers (BERT) and bidirectional gated recurrent unit (BiGRU) with conditional random field (CRF) to form the BERT-BiGRU-CRF model, and changing the input of the model from a single feature vector to a multi-feature vector (MF) after stitching character vector features, word vector features, word length features, and part-of-speech (pos) features; BiGRU is introduced to extract the contextual features of shipping enterprise credit texts. Finally, CRF completes the sequence annotation task. According to the experimental results, using the BERT-MF-BiGRU-CRF model for NER of shipping enterprise credit text data, the F1 Score (F1) reaches 91.7%, which is 8.37% higher than the traditional BERT-BiGRU-CRF model. The experimental results show that the BERT-MF-BiGRU-CRF model can effectively perform NER for shipping enterprise credit text data, which is helpful to construct a credit knowledge graph for shipping enterprises, while the research results can provide references for complex entities and nested entities recognition in other fields.