Entity Anomaly Recognition Method Based on GCNN and GRU

Abstract

Named entity recognition (NER) model can recognize the normal entities, but cannot provide any hints for the missing entity or the entity in the incorrect location, which cannot meet the extensive requirements on the text entities in the information processing and the archiving analysis field. Combined with the specific context characteristics of the entity anomaly recognition, a method for the entity location anomaly and the entity absence anomaly detection (NER-EAD) integrating GCNN (gated convolutional neural network) and GRU (gated recurrent unit) and its training data construction method are proposed, which is based on the structure of the named entity recognition model with the pre-trained language model. GCNN extracts more character context features to improve the model performance in identifying abnormal entities. The method integrates the semantic feature output of the convolutional neural network and the recurrent neural network to comprehensively extract the features of normal entities and the abnormal entities. Experiments show that NER-EAD reaches average F1 of 90.56%, 85.56% and 80.92% in the normal entity recognition, the entity location anomaly detection and the entity absence anomaly detection, respectively, surpassing the existing named entity recognition model architecture. Additionally, the ablation experiment proves the semantic feature extraction ability of the fusion network of GCNN and GRU.

Keywords

• named entity recognition (ner); gated convolutional neural network (gcnn); gated recurrent unit (gru); abnormal detection