## 2 Related Works  
### Named Entity Recognition  
Named entities are proper nouns, such as personal, place, and organization names. NER is a fundamental task in natural language processing, which automatically identifies and classifies predefined specific language expressions in an unstructured natural language text, primarily proper nouns. NER can be used for various purposes, such as Q&A, chatbots, reservation systems, and customer service interactions. Various attempts have been made to address NER tasks (e.g., Q&A and chatbots). The three main approaches to addressing NER tasks used before the advent of deep learning are rule-based, unsupervised, and feature-based supervised learning. Rule-based NER uses domain-specific dictionaries or syntax-lexicon patterns and has high accuracy in specific domains; however, it lacks recall, making it difficult to transfer data to other systems. Unsupervised learning mainly achieves NER through clustering. It extracts named entities from groups based on contextual similarity. The main concept is that mentions of named entities can be identified through vocabulary resources, patterns calculated from large corpora, and statistics. Other studies have used supervised learning to apply multiclass classification techniques or sequence labeling, such as NER [18]. Other studies have used deep learning models, such as convolutional neural networks and recurrent neural networks (RNNs), including long short-term memory. In South Korea, in 2018, a model that combined a bidirectional RNN and conditional random field with ensemble techniques showed significant performance improvement in the Naver NLP Challenge [19]. Transformer-based pretrained language models are a new paradigm to address the NER challenge and have significantly improved NER task performance [18]. Based on existing patterns and morphological analysis, traditional personal information detection systems face problems related to low detection rates, labor, and time consumption. To improve the detection rate through iterative experiments, Naver nlpchallenge proposed a machine learning method to train patterns and types of personal information from several electronic documents [20]. Personal information cannot be identified using regular expressions in atypical text data or existing NERs. Therefore, [PERSON] et al. [15] proposed a method to train the BERT model using speaker information and label two tags for a single phrase. The intention was to tag speaker A as NAME A (NMA), speaker B as NAME B (NMB), and the name of the person who did not participate in the dialog as NAME OTHERS (NMO) [17]. However, the scope of that study was limited to testing names among personal information items. Furthermore, the quality and size of the dataset were insufficient for personal information detection. [PERSON] et al. [16] proposed a model for detecting personal information. This model uses the intended information of a sentence as additional information in the named entity learning process and a de-identification technique that considers the utility of personal information. The model uses Korean pretrained language models to simultaneously classify the intent of sentences and detect personal information in atypical text data. However, the personal information tag set used for identification consisted of only seven tags, making it difficult for the model to detect various types of personal information. [PERSON] and [PERSON] [21] used deep learning techniques to develop an NER model that focuses on criminal investigation. They collected texts from the criminal investigation domain and redefined the classification of named entities required for crime analysis. They conducted an experiment where they categorized the domain of criminal investigation into 9 main categories and 56 subcategories. Their experimental results showed that all categories were identified exceptionally well.