## 5 Conclusions  
In this study, we analyzed 33 out of 39 items of personal information to identify personal information from dialog-type texts and constructed a personal information tag set. Some personal information items could not be accurately detected owing to the limitations of the general NER tag set. We established guidelines for the defined personal information categories to facilitate data construction and collected dialog-type text data. According to the data structure specification, we converted the collected data into JSON format, which is suitable for AI learning. Subsequently, we performed experiments on the PNEs using the BERT and ELECTRA models. The best performance in this study was 94.3%, based on F1-score, when KPF-BERT was trained by setting batch sizes to 4 and Max Sequence Length to 512, and increasing the length of sentences from sentence units to conversation units [35]. In detecting 33 specific items of personal information, a high performance of over 90 percent was achieved on numerical data with patterns or limited forms of personal information. Unique identification information, blood type, mobile phone number, email, URL, and IP had a 100 percent detection rate, with an F1-score of 1.0. For 16 items, including date of birth, height, and weight, the recall was 1.0, indicating that these items had a consistent pattern. However, many items, including nicknames, clubs/societies, places, and job titles/positions had a lower detection rate than other categories owing to their diverse forms and usage, making prediction challenging. Thus, additional training data for items with low detection rates must be collected, and detection performance needs to be improved through additional learning. Through this process, we established a foundation to flexibly address any changes in the personal information section or the need for additional information for learning and to proactively respond to potential personal information leakage. Named entity recognition research has generally been conducted based on English, with a tendency to focus on model performance while overlooking aspects such as data collection and preprocessing. In this study, we explored procedures such as data collection, preprocessing, and the conversion of training data to provide directions on the application of entity recognition research to non-English languages. The developed NER differs from previous NERs and addresses the issue of personal information detection. However, detecting personal information presents challenges in assessing the risk of personal information leakage. Future research should explore topics beyond simple personal information detection and evaluate the risk of data leakage based on the detected personal information. Furthermore, detecting personal information requires research experiments using generative pretrained transformers and large language models. We anticipate that further research building upon our model will be able to prevent personal information leakage in various systems that generate large-scale text data. Conceptualization, T.K. and S.J. ; resources and data curation, Y.C. and H.S. ; methodology, S.J., Y.C., and H.S. ; validation, S.J. and H.W. ; investigation, Y.C. and H.S. ; formal analysis, H.S. and Y.C. ; visualization, Y.C. and H.S. ; writing--original draft, S.J. ; writing--review and editing, S.J. and H.W. ; project administration, T.K. and S.J. ; funding acquisition, T.K. All authors have read and agreed to the published version of the manuscript. This study was funded by the Personal Information Protection Commission of the Republic of Korea and the Korea Internet & Security Agency (KISA), grant number 1781000017. Not applicable. The datasets presented in this article are not readily available because the data are part of an ongoing study. Requests to access the datasets should be directed to the email address [EMAIL_ADDRESS]. This study was supported by the Personal Information Protection Commission of the Republic of Korea and the Korea Internet & Security Agency (KISA).