Development of methods for extracting information from pharmacy line using conditional random fields

The paper considers the solution to the problem of extracting information from short lines of pharmacological orientation in Russian language. As an example, pharmacy lines are used, from which you need to extract the full name of the drug, manufacturer, form of issue, dosage, number of pieces in a package and some other parameters. To extract this information, a conditional random field (CRF) algorithm was used. There was also created a method for preliminary standardization of the strings to bring string tokens to a single form. More than seven thousand pharmacy lines were marked for the experiments and 2 CRF models were trained - with and without preliminary standardization of the lines. For the model with standardization, the following results were obtained: accuracy for different data sets is 0.95 (on the validation set) and 0.89 (on the test set). For the model without standardization, the accuracy is 0.95 (on the validation set) and 0.87 (on the test set). Copyright © 2021 for this paper by its authors.