Robust Chinese Named Entity Recognition Based on Fusion Graph Embedding

Abstract

Named entity recognition is an important basic task in the field of natural language processing. The current mainstream named entity recognition methods are mainly based on the deep neural network model. The vulnerability of the deep neural network itself leads to a significant decline in the accuracy of named entity recognition when there is adversarial text in the text. In order to improve the robustness of named entity recognition under adversarial conditions, this paper proposes a Chinese named entity recognition model based on fusion graph embedding. Firstly, the model encodes and represents the phonetic and glyph information of the input text through graph learning and integrates above-multimodal knowledge into the model, thus enhancing the robustness of the model. Secondly, we use the Bi-LSTM to further obtain the context information of the text. Finally, conditional random field is used to decode and label entities. The experimental results on OntoNotes4.0, MSRA, Weibo, and Resume datasets show that the F1 values of this model increased by 3.76%, 3.93%, 4.16%, and 6.49%, respectively, in the presence of adversarial text, which verifies the effectiveness of this model.

Enhanced Chinese Domain Named Entity Recognition: An Approach with Lexicon Boundary and Frequency Weight Features

“…This notably improves various metrics for natural language processing (NLP) tasks. In the field of NER, significant achievements have been made by methods based on fine-tuning pretrained models, like BERT [19,20], BERT-CRF [21,22], and BERT-BiLSTM-CRF [23]. Pretrained models can learn contextual features of text on the basis of training on large-sample data, enabling fine-tuning in situations with limited annotated data and subsequently learning contextual features for downstream tasks.…”