# Improving Named Entity Recognition for Social Media with Data Augmentation
## 2 Related Work
### Data Augmentation  
Obtaining the high quality of text representation is crucial for good model performance for many NLP tasks. However, due to sparse data and category imbalance in social media NER, the generalization ability and robustness of these models are still challenging. To address these challenges, researchers have proposed a variety of approaches to data augmentation, for example, data augmentation using synonyms, antonyms, and word form variations; semantic transformation based on semantic role annotations; and the generation of new samples via language models.  
Data augmentation is a method to increase data diversity, include synonym replacement, antonym replacement, random insertion, random deletion, etc. [PERSON] et al. [7] used synonym dictionaries for data enhancement and classification, but limitations in dictionary size and lexicality prevented the replacement of all words. [PERSON] et al. [8] replaced names and places with aliases or related words to generate similar but not identical data, but their method performed word-by-word substitution independently, potentially damaging semantic fluency with too many substitutions.  
Semantic transformation is the transformation of semantic information from the original training data to extend the training data. These methods include inserting new entities into the text data, inserting qualifiers of entity types around the entities, and transforming the entity types. [PERSON] and [PERSON] et al. [10] used word embedding to enhance text classification, replacing each original word with a similar word based on cosine similarity to ensure diverse augmented data. [PERSON] [27] combined lexicon and word embedding methods to build a lexicon of relevant words for each word and continuously improved it during training, solving the issue of limited applicability to specific words. However, these methods may not effectively handle multiple words and could still harm semantic fluency.  
Language model generation is the use of pre-trained language models (e.g., GPT, BERT, etc.) to generate new training samples. In NER tasks, this method can generate new data that are similar but not identical to the original training data. [PERSON] et al. [11] developed a method for data augmentation by using the word splitter of BERT to obtain multiple word pieces from each piece of raw data and then constructed a candidate set of replacement words for each word piece. The candidate set was constructed by using a combination of the word vector-based method and the mask model-based method. The mask language model considers contextual information during prediction to solve the problem of multiple meanings of words and generates semantically fluent sentences. However, this method often requires heuristics to determine the position of the mask to ensure that the augmented sentence retains the original semantics. Overall, this method is effective in generating diverse and semantically fluent sentences but requires careful consideration of the mask position during augmentation.  
The key difference between our approach and others is that we do not rely on direct data expansion or semantic transformation. Instead, we fuse the entities that have been enhanced with additional data after training, which helps to avoid any negative effects on the inter-contextuality of the data. This also ensures that the additional entity information takes into account the association between different contexts, which can lead to more accurate results in NER.