# Improving Named Entity Recognition for Social Media with Data Augmentation
## 2 Related Work  
### Named Entity Recognition  
Named entity recognition has a rich history in research, with the majority of models regarding it as a sequence labeling task, which identifies named entities with specific meaning from text, such as people's names, locations, organizations, dates, times, and so on, to aid in comprehending the meaning and structure of the text. Traditional sequence labeling models are based on Hidden Markov Models (HMM) [15] or probabilistic graph models such as CRF [16].  
The emergence of deep learning has sparked considerable interest among researchers in neural-based models. Recent research on NER has explored various models, including convolutional neural networks (CNN) [17; 18; 19], recurrent neural networks (RNN) [20], and transformers [21; 22]. These models use contextual information and semantic representations to automatically learn features and rules, eliminating the need for manual design and overcoming its drawbacks. Additionally, the emergence of pre-trained language models, such as BERT [12] and GPT [23], has further improved the effectiveness of NER. Thesemodels are pre-trained on large-scale unlabeled texts and fine-tuned on labeled data, which can significantly improve the accuracy and robustness of NER.  
However, the existing NER methods for social media often struggle with informal language, spelling errors, and the use of non-standard abbreviations and expressions. These factors contribute to the scarcity and sparsity of labeled data, which renders it more difficult to train NER models that can achieve high accuracy on social media text. [PERSON] et al. [22] implement bidirectional contextual information by pre-training a language model. It predicts the possible words at a location by randomizing the input sequence and then trains a word vector with context. [PERSON] et al. [24] enhance the NER model by using syntactic information or semantically related text. [PERSON] et al. [25] assist the contextual model training by introducing social media images. [PERSON] et al. [26] obtain more local features and location information via multi-window loops and combining global information and multiple local features to predict entity labels. These existing research works have certainly helped to improve the performance of NER on social media data. However, they have their limitations. Pre-training the model on large-scale data can introduce noise to the already noisy social media data, making it difficult to accurately capture the features of entities. Similarly, using semantic or image information via direct introduction can also introduce noise and affect the ability of the model to identify entities accurately. Additionally, some of these methods may suffer from overfitting to specific entities or situations, reducing their generalizability to different domains and datasets. Therefore, our proposed method aims to address these limitations by utilizing data augmentation techniques that are specifically designed for social media data, allowing the model to capture the diverse and nuanced language used in social media while avoiding the introduction of noise.