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Found 8 papers, 1 papers with code
Automatic Summarization of Russian Texts: Comparison of Extractive and Abstractive Methods
The key problem of the argument text generation for the Russian language is the lack of annotated argumentation corpora.
Argumentative Text Generation in Economic Domain
The key problem of the argument text generation for the Russian language is the lack of annotated argumentation corpora.
RuArg-2022: Argument Mining Evaluation
Argumentation analysis is a field of computational linguistics that studies methods for extracting arguments from texts and the relationships between them, as well as building argumentation structure of texts.
Collocation2Text: Controllable Text Generation from Guide Phrases in Russian
The method is based on two interacting models: the autoregressive language ruGPT-3 model and the autoencoding language ruRoBERTa model.
Does BERT look at sentiment lexicon?
We fine-tune RuBERT on sentiment text corpora and compare the distributions of attention weights for sentiment and neutral lexicons.
Lexicon-based Methods vs. BERT for Text Sentiment Analysis
The purpose of the article is to study the performance of the SO-CAL and SentiStrength lexicon-based methods, adapted for the Russian language.
Traditional Machine Learning and Deep Learning Models for Argumentation Mining in Russian Texts
Argumentation mining is a field of computational linguistics that is devoted to extracting from texts and classifying arguments and relations between them, as well as constructing an argumentative structure.
Current Landscape of the Russian Sentiment Corpora
Currently, there are more than a dozen Russian-language corpora for sentiment analysis, differing in the source of the texts, domain, size, number and ratio of sentiment classes, and annotation method.
