## 2 Previous Studies  
NER tasks were previously resolved by considering them as sequence labeling problems. In this regard, most previous NER systems adopted machine learning (ML) models, such as decision trees [3], maximum entropy [4], and conditional random fields [5]. To improve the NER performance, these ML-based systems focused on feature engineering methods, such as word n-grams, part-of-speech n-grams, lexical clues, and knowledge look-up (to determine whether an input word exists in an external knowledge base) [6]. With the recent success of deep learning (DL), many DL-based NER systems have been proposed to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Coarse-Grained NE**} & \multicolumn{2}{c}{**Fine-Grained NE**} \\ \hline
**Class** & **Example** & **Class** & **Example** \\ \hline \multirow{3}{*}{Location} & USA, & Country & Korea \\  & Washington, D.C., & Province & Gangwon-do \\  & Memorial park & City & Seoul \\ \hline \multirow{2}{*}{Date} & Thanksgiving day, & Year & 2020 \\  & 24 April 2020 & Duration & 2019–2021 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of two-level NE categories.  
reduce the labor required for feature engineering [7, 8]. These DL-based systems performed reasonably by using various distributed representations (e.g., word embeddings and character embeddings) instead of expensive knowledge features. Although many researchers have studied NER, it is not easy to find studies on fine-grained NER with hundreds of NE classes, especially for non-English languages. The authors in [9] presented a fine-grained entity recognizer that solved a multi-label, multi-class classification problem by adapting a Perceptron model. The Ref. [1] conducted an empirical study to develop a fine-grained NER model that is robust across various settings, such as the number of NE classes and the size of the training dataset, by using a bidirectional long short-term memory model with a conditional random field layer (BI-LSTM-CRF) [10]. They reported that a fine-grained NER model that is effective for English is not necessarily effective for Japanese. This showed that a fine-grained NER task has language-dependent characteristics. To overcome a lack of training data, [11] proposed a method using a language model and an expensive knowledge base in a fine-grained NER task. The Ref. [12] proposed a novel adversarial multitask learning framework in which POS tagging is performed together with NER in Chinese. The Ref. [13] proposed a sequence-to-sequence model to consider the entire meaning of an input sentence. They used BI-LSTM as the encoder to equally process the past and future information of an input sentence. Then, they added a self-attention mechanism to address the long-term dependency problem in a long sequence. The Ref. [14] showed that the embeddings of decomposed NE labels can be effectively used to improve the performance of instances with low-frequency NE labels.The Ref. [15] proposed a model that included the initial encoding layer, the enhanced encoding layer, and the decoding layer, combining the advantages of pre-training model encoding, dual bidirectional long short-term memory (BiLSTM) networks, and a residual connection mechanism. The Ref. [16] proposed a Chinese fine-grained NER method based on a language model and model transfer considering active learning (MTAL) to research a few labeled data. The Ref. [17] proposed a Cognitive Impairment model that can filter, study, analyze, and interpret written communications from social media platforms. The Ref. [18] proposed a label attention network (LAN) that captured possible long-term label dependency by utilizing an attention mechanism and label embedding. We adopted this LAN and proposed a dual-stacked LAN, with the lower for coarse-grained NER and the upper for fine-grained NER.