## 1 Introduction  
Recently, text mining has garnered significant academic attention [1; 2] and various web-based annotation tools have been proposed [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18]. In particular, text mining technologies have evolved to provide automatically generated pre-annotations using machine learning, increasing the amount of data curation to unprecedented levels. However, the amount of generated training data remains limited; only data from general fields are available, rather than those on specialized topics such as diseases and genes [19; 20; 21; 22]. A tagging tool called ezTag, which annotates biological parameters, was developed to address this shortcoming [23], but it lacks adequate project management as well as support for collective annotation by multiple users. To overcome these limitations, a tagging tool called TeamTat was proposed [24], which is a web-based annotation tool that allows multiple users to manage annotation projects together. Moreover, the comments of independent users can be collected and viewed simultaneously.  
In our study, the BioBERT model, which integrates biomedical text corpora with bidirectional encoder presentations from transformers (BERT) models, is employed to construct a web system [25]. In the tasks of named-entity recognition (NER), relation extraction (RE), and question answering (QA), the BioBERT model outperforms current state-of-the-art models. Consequently, our approach surpasses earlier text mining tools in terms of entity tagging. In addition to delivering entity tagging options comparable to those supplied by other text mining systems, our system enables and reflects users' ability to tag individual entities themselves. Users no longer need to spend substantial time and effort on entity tagging with our system. Furthermore, we enable the collection of huge volumes of training datasets in specific domains (biomedical domains) as opposed to general domains, hence substantially increasing the quantity of data curation.  
In this paper, we integrate a tagging tool into NER and RE web interfaces using BERT. Moreover, we improve the quality of NER results by fine-tuning the deep learning model. The system architecture of the proposed tagging tool is depicted in Figure 1. First, the user inputs the new NE annotation, comprising a list of words and categories (e.g., diseases, genes/proteins) of named entities selected by the user, via the web. Second, in the NE annotation collection, users can download and collect new NE annotations. The new NE annotation collection includes a list of words and categories (e.g., disease, genes/proteins) of named entity selected by the user. After the amount of input NE annotation data reaches a certain threshold, users can fine-tune the deep learning model to create a new pretrained model. This allows the system to yield better NER results. Our main contributions are as follows:  
* Our web service is a biomedical text mining tool that uses deep learning based high-performance BioBERT NER and RE models;
* The NER result is displayed in a unique color, and the RE result is shown in a graph;
* We introduced a tagging tool system to our service so that users can tag the entities they desired;
* Our web service can download newly tagged annotations and use it as a dataset for retraining. If this is used for retraining, better NER results can be used through the new pretrained model;
* This service is freely available at [[http://nertag.kw.ac.kr](http://nertag.kw.ac.kr)]([http://nertag.kw.ac.kr](http://nertag.kw.ac.kr)) (accessed on 10 October 2022)