Adverse drug reactions can have serious consequences for patients. Social media is a
source of information useful for detecting previously unknown side effects from a drug since …

Detection of new adverse drug reactions is intended to both improve the quality of
medications and drug reprofiling. Social media and electronic clinical reports are becoming …

The paper presents an approach to named entity oriented sentiment analysis of Russian
news texts proposed during the RuSentNE evaluation. The approach is based on …

An experimental work on the analysis of effectiveness of neural network models applied to
the classification of adverse drug reactions at the entity level is described. Aspect-level …

In the field of jurisprudence, document management plays a key role. Lawyers have to
process a large volume of text documents in order to find necessary information. In this …

Detection of adverse drug reactions in postapproval periods is a crucial challenge for
pharmacology. Social media and electronic clinical reports are becoming increasingly …

We study topic models designed to be used for sentiment analysis, ie, models that extract
certain topics (aspects) from a corpus of documents and mine sentiment-related labels …

The research presents the methodology of improving the accuracy in sentiment classification
in the light of modelling the latent semantic relations (LSR). The objective of this …

This paper presented the methodology of Textual Content Classification, which is based on
a combination of algorithms: preliminary forming a contextual framework for the texts in …

В данной работе представлено экспериментальное исследование эффективности
ряда моделей нейронных сетей для задачи классификации побочных эффектов на …