Interview with Svetlana Efimova (Movie Going in St. Petersburg in the 1970s)

This is the audio file, transcript, and analysis of the interview with Svetlana Efimova conducted by Sabina Abdullayeva and Jennifer Stolz and transcribed and analyzed by Sabina Abdulayeva, Mary Burke, and Jennifer Stolz. The interview was conducted in July 2010 in St. Petersburg, Russia.Russian and Post-Soviet Studies Program (RPSS

Movie Going in St. Petersburg in the 1970s

This is the audio file, transcript, and analysis of the interview with Viktoria Sorkina conducted by Maggie Burke and transcribed and analyzed by Mary Burke, Alex McGrath and Jennifer Stolz. The interview was conducted in July 2010 in St. Petersburg, Russia

CLOUD SERVICES FOR NATURAL LANGUAGE PROCESSING

The paper presents the results of experiments conducted with the aim of a comparative analysis of the performance of the existing cloud services for natural language processing in Russian. The article provides an overview of 10 cloud services: TextRazor, RosetteTextAnalytics, EurekaEngine, CloudNaturalLanguage, Texterra, Pullenti, NER-ru, UDPipe, AOT, DeepPavlov. Quantitative studies of their performance were made for 6 of them. In the process of evaluating services, the execution of such functions as the part of speech tagging, sentiment analysis, named entity recognition and the categorization of texts were analyzed. For a comparative assessment of the quality of the services, the following competition materials were used: factRuEval-2016 (named entities), AlemResearch (sentiment) and the corpora, Taiga and OpenCorpora (part of speech). The named entities recognition quality was evaluated by calculating Accuracy, Precision, Recall, and F1 parameters. As a result of the study, it was shown that when solving natural language text processing tasks in Russian, the best result is shown by the EurekaEngine service for recognizing named entities and sentiment analysis of the text, RosetteTextAnalytics service proved best in part of speech tagging the and TextRazor service in text categorization