# Improving Named Entity Recognition for Social Media with Data Augmentation
###### Abstract  
Social media is important for providing text information; however, due to its informal and unstructured nature, traditional named entity recognition (NER) methods face the challenge of achieving high accuracy when dealing with social media data. This paper proposes a new method for social media named entity recognition with data augmentation. First, we pre-train the language model by using a bi-directional encoder representation of the transformer (BERT) to obtain a semantic vector of the word based on the contextual information of the word. Then, we obtain similar entities via data augmentation methods and perform substitution or semantic transformation on these entities. After that, the input into the Bi-LSTM model is trained and then fused and fine-tuned to obtain the best label. In addition, our use of the self-attentive layer captures the essential information of the features and reduces the reliance on external information. Experimental results on the WNUT16, WNUT17, and OntoNotes 5.0 datasets confirm the effectiveness of our proposed model. social media; named entity recognition; data augmentation; BERT +
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and diversity of training data [7; 8; 9]. Additionally, semantic transformation techniques can make use of semantic role annotation information to perform semantic transformation, thereby further increasing data diversity and richness [10]. In addition to traditional data augmentation techniques, language model generation has also demonstrated its effectiveness as a data augmentation method [11] by generating new samples by using language models, it can help alleviate the impact of insufficient training data while improving the generalization ability of the model. To address the above issues and improve the robustness and accuracy of NER in social media, this paper proposes a framework that is based on data augmentation for social media NER (DA-NER). First, we use a pre-training language model, the bidirectional encoder representations from transformers (BERT) [12], to extract the semantic vector of each word based on its contextual information. Then, we obtain similar entities via data augmentation methods and subject the entities to replacement or semantic transformation. Specifically, we calculate the cosine similarity between the entity embeddings in the training data and the entities of the predefined knowledge base by pre-training the word embedding model GloVe [13]; then, we select the entities with high cosine similarity and weight them to the corresponding entities to obtain the semantic vector after data augmentation. This balances data augmentation and context to improve model performance and generalization ability. The two vectors are then fed into the Bi-LSTM-CRF [14] model for training and then fused and fine-tuned to obtain the optimal labels. In addition, our use of the self-attention layer captures the underlying information of the features and reduces the dependence on external information. Our approach is experimented on three widely used benchmark datasets in the social media domain. The experimental results show that our approach of the DA-NER model is effective and achieves state-of-the-art results on all datasets. The contributions of this paper can be concluded as follows:  
* We apply data augmentation techniques to social media NER, which effectively address the challenges of data sparsity and category imbalance. Experimental results demonstrate that the incorporation of data augmentation can significantly improve entity recognition performance and enhance the generalization and robustness of the model. * In this paper, the attention mechanism is integrated into the Bi-LSTM model, which assigns weights to different words use the selection type. This enables the model to leverage contextual semantic associations and effectively address the challenges associated with acquiring local features. * We conducted extensive experiments on three benchmark datasets for social media NER.