articles

All enterprises engaged in exploration activities on the territory of the Russian Federation, are facing the need to formulate tasks for the mine surveyor service and control their execution. It affects enterprise’s workflow process. Due to it, a problem of organization of efficient document processing in electronic document management systems (timely identification of documents containing mine surveying data) takes place. The article presents possible solution of this problem – automated document classification system into EDMS in the form of optional add-on for 1C:Document Management. Within the classification system creation a preprocessing script for primary document texts, including cleaning, lemmatization, stop words removing, as well as preparation of input features for the classifier were developed and implemented. Applicability of different machine learning algorithms to solution of considering classification problem was studied, the values of hyperparameters providing the highest value of the ROC AUC metric were determined. The quality of all obtained models was assessed using metrics Precision, Recall and F-measures, the stability of the classification quality to changes in the input data was investigated. The identified problem of instability of classification results was solved by building and implementing a machine learning model in the form of ensemble of classifiers. Classification model (an ensemble of clusters) was tested on the set of real documents of Gazprom nedra Ltd; classiffication quality on the test sample by ROC AUC metric was 0,91. Except the classification module itself, developed system contains the storage database for learning outcomes, function library for organization of work with the database and API interfaces allowing to process classification requests, coming from external systems. These API interfaces, in particular, implement the ability to load saved trained models, validate data coming from external systems, preprocess input text documents, train new models and assess their quality, save both trained models and the results of their testing. Also the possibility of the additional training of the models on a new data was realized.