# Label-Guided Data Augmentation for Chinese Named Entity Recognition
## 5 Experiments Setting
### Baseline Models  
This section compares the proposed LGDA model with recent baseline models related to Chinese named entity recognition tasks to validate the superiority of the proposed model. A summary of the baseline models is as follows:  
* **BERT (2018)**[10]: BERT, a pre-trained language model with a labeled classifier, fully encodes words and further adds a CRF label decoding layer.
* **Lattice LSTM (2018)**[11]: A dictionary-based Chinese named entity recognition method using a lattice structure that fully utilizes the information of characters and character sequences.
* **BERT-MRC (2019)**[15]: It reconstructs the named entity recognition task as a reading comprehension task, with entity labels as query questions and answers as entities, promoting entity extraction with this query prior knowledge.
* **MECT (2021)**[20]: A cross-Transformer model based on multi-modal data embeddings that integrates the structural information of Chinese characters to improve the effect of low-resource Chinese named entity recognition.
* **Multi-Grained (2021)**[21]: A multi-grained distillation method for named entity recognition, using an improved Viterbi algorithm to construct pseudo-labels and transfer knowledge from teacher models to lightweight models.
* **LEAR (2021)**[8]: It views the named entity recognition task as a question-answering task, using preset questions for entity extraction, and proposes using label knowledge to improve text representation.
* **PCBERT (2022)**[9]: A two-stage model for low-sample Chinese named entity recognition: the first stage uses P-BERT with prompt templates to extract implicit label features, and the second stage fine-tunes a lexicon-based C-BERT with label expansion functions.
* **XLNet-Transformer-R (2023)**[16]: A model that combines XLNet and a Transformer encoder with relative positional encodings to process long text and contextual information and uses R-Drop to prevent overfitting.
* **MW-NER (2023)**[17]: A model for Chinese NER that fuses multi-granularity word information by using a strong-weak feedback attention mechanism and combines it with character information via two fusion strategies to enrich semantics and reduce segmentation errors.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Category** & **Tr Set** & **Val Set** & **Test Set** \\ \hline PER & 16,105 & 1510 & 1973 \\ LOC & 3486 & 1657 & 2877 \\ ORG & 19,481 & 1090 & 1331 \\ \hline
**Totally** & **70,446** & **4257** & **6181** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistics on the number of various named entities in MSRA.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Category** & **Tr Set** & **Val Set** & **Test Set** \\ \hline PER & 4391 & 1652 & 1864 \\ LOC & 928 & 483 & 491 \\ GPE & 4960 & 3166 & 3452 \\ ORG & 3088 & 1649 & 1877 \\ \hline
**Totally** & **13,367** & **6950** & **7684** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistics on the number of various named entities in OntoNotes 4.0 (Chinese).  
* **E-strPron (2024)**[18]: A method that integrates pronunciation, radical, strokes, writing order, and lexicon features, using two cross-Transformers to capture attention and a soft fusion module to combine mutual attention.