T-Soft (Czechia)

Recent directions for offensive language detection are hierarchical modeling, identifying the type and the target of offensive language, and interpretability with offensive span annotation and prediction. These improvements are focused on English and do not transfer well to other languages because of cultural and linguistic differences. In this paper, we present the Korean Offensive Language Dataset (KOLD) comprising 40,429 comments, which are annotated hierarchically with the type and the target of offensive language, accompanied by annotations of the corresponding text spans. We collect the comments from NAVER news and YouTube platform and provide the titles of the articles and videos as the context information for the annotation process. We use these annotated comments as training data for Korean BERT and RoBERTa models and find that they are effective at offensiveness detection, target classification, and target span detection while having room for improvement for target group classification and offensive span detection. We discover that the target group distribution differs drastically from the existing English datasets, and observe that providing the context information improves the model performance in offensiveness detection (+0.3), target classification (+1.5), and target group classification (+13.1). We publicly release the dataset and baseline models.

It has been an accepted tendency in the industrial practice since the last third of the 20th Century to decrease the living labour demand of the manufacturing processes. One of the main tools of it is the automation of production. In the design, the construction and the programming of the developed automata, the results of electronics, informatics and mechatronics can be used. All of the scientific fields mentioned above undergo intensive development. By the application of the results achieved in the design of the applications, the monotonous, dangerous and tiresome work not requiring expertise can be assigned to machines. In the longer run, the application of the automata is beneficial also in financial aspect, since the costs of living labour are continuously growing, while that of the mechatronic and technological devices show a rather decreasing tendency. Reliability is also an important aspect. It is well known for everyone that a well-designed and programmed automatic device performs its task without mistakes. If there is any objection or breakdown, on the basis of the currently valid safety technology rules, the machine stops, does not trigger any damage and after its repair, work can be continued.

Background: From a mathematical point of view, the problems of medical diagnostics are the tasks of data classification. It is important to understand how significant distortions can contribute to the result of classification errors in the collection of primary diagnostic information, in particular, the results of biochemical tests. Aims: Determination of the dependence of the prediction result on the variability of the primary diagnostic information on the example of the model classifier. Materials and methods : The case-control study enrolled patients who were divided into 2 groups: the main (diagnosed with lung cancer, n=200) and the control group (conditionally healthy, n=500). Questioning and biochemical saliva study was performed in all participants. Patients of the main group and the comparison group were hospitalized for surgical treatment, after which carried out the histological verification of the diagnosis. The biochemical composition of saliva is determined spectrophotometrically. Based on the data obtained, a model classifier for the diagnosis of lung cancer (a random forest) has been constructed. In each parameter underlying the classifier, deviations were made in the specified range (±1–5%, ±5–10%, ±10–15%), creating synthetic images. Then, the results of the classification were evaluated by the cross-validation method. Results: The basic diagnostic characteristics of the model classifier are determined (sensitivity ― 72.5%, specificity ― 86.0%). As the deviations of synthetic images from the baseline increase, diagnostic characteristics deteriorate with the general classification. However, the result of a confident classification, on the contrary, gives higher values (sensitivity ― 81.8%, specificity ― 93.1%). In case of a confident classification, similar images that fall into different classes according to the classification results are deleted, whereas in the case of a general classification, they are taken into account. The difference between methods of classification is associated with the presence of images on which the classifier gives the result of belonging to the class in the range of 0.45–0.55. Therefore, it is necessary to introduce a third class into the classifier, the so-called gray zone (0.4–0.6), since the probability of making an erroneous diagnosis in this area is significantly increased. Conclusions: The obtained results allow to conclude that the measurement error in the range (±1–15%) does not significantly affect the quality of the classification.