Tatiana Litvinova

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Automatic extraction of information about authors of texts (gender, age, psychological type, etc.) based on the analysis of linguistic parameters has gained a particular significance as there are more online texts whose authors either avoid providing any personal data or make it intentionally deceptive despite of it being of practical importance in(More)
Authorship profiling which is a process of the extraction of information about the unknown autohr of a text (demographics, psychological traits, et al.) based on the analysis of linguistic parameters, is a problem of great importance. Research in authorship profiling has always been constrainted by the limited availability of training data since collecting(More)
Psychology studies show that people detect deception no more accurately than by chance, and it is therefore important to develop tools to enable the detection of deception. The problem of deception detection has been studied for a significant amount of time, however in the last 10-15 years we have seen methods of computational linguistics being employed(More)
In the present article, we consider a problem to evaluate the gain in accuracy of using deep learning network for two language tasks: the automatic text classification according to the authors gender and to identify text sentiment. A preexisting corpus of Russian-language texts RusPersonality labeled with information on their authors (gender, age,(More)
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