According to BleepingComputer, a hacking forum leaked the personal information of 2.6 million users of Duolingo, the world's largest language learning site [12]. The data were scrapped using a publicly available application programming interface (API) and sold on the breached hacking forum. The leaked data contained public information, such as login IDs and real names, and private information, such as emails and completed courses. Furthermore, in 2021, Facebook experienced a data breach through a friend request API bug, exposing the phone numbers of over 500 million users. Similarly, Twitter also experienced personal information leakage, including the email addresses of millions of users. These examples highlight the risks of using data generated through language-based services, such as SNS and chatbots, data analysis, and AI tasks. These issues can be broadly categorized into two main groups: The first issue involves data collection and acquisition. Data are primarily collected by scraping publicly available text on the internet, such as social networking sites. This may result in privacy breaches. The second issue pertains to using AI models trained to collect data. There is a risk of personal information being regenerated from the inference results of AI models. The risk increases significantly with the amount of data [13]. Accurately detecting personal information in data is paramount in addressing these issues, as are the crucial steps of de-identifying or pseudonymizing these data. Previous research has extensively used named entity recognizers for this task. However, these conventional methods are prone to errors of unrecognition and misrecognition, leading to incomplete or inaccurate detection of personal information [14]. Challenges arise when detecting unique identifiers, such as resident registration and passport numbers. Furthermore, certain attributes, including gender and blood type, remain undetectable. These limitations underscore the need for advanced personal information processing solutions. Named entity recognition (NER) studies have been primarily conducted with data in English. Owing to the predominance of English in publicly available datasets, working with English alternatives can present significant challenges [15]. NER research based on the Korean language is inadequate, although Korean is becoming globally used. In recent research on personal information detection using a Korean named entity recognizer, attempts were made to use additional information, such as sentence intention or speaker information. However, the study faced limitations owing to insufficient data [16; 17]. Therefore, defining a privacy named entity (PNE) tag set and collecting and processing data will positively impact AI-based natural language processing research outside the English-speaking world. We selected 33 personal information items to identify personal information in interactive texts and built a set of personal information tags. We then collected data using data-building guidelines and converted them into JSON format suitable for AI learning. We selected bidirectional encoder representations from the transformer (BERT) and efficiently trained an encoder that accurately classifies token replacements (ELECTRA) as the pretrained language models. These were implemented and outperformed general object name detectors in personal information detection. The remainder of this paper is organized as follows. Section 2 explores research related to NER and personal information. Section 3 presents the establishment of a PNE tag set, interactive data collection and processing, the structuring and building of datasets, the construction of learning models, and the preprocessing of training data. In Section 4, a false detection case analysis, conducted to determine areas of improvement, is discussed, and the performance of the model is verified. Section 5 summarizes the research findings and proposes future research directions.