Sentiment analysis has become a powerful tool in processing and analysing expressed
opinions on a large scale. While the application of sentiment analysis on English-language …

While automated question answering systems are increasingly able to retrieve answers to
natural language questions, their ability to generate detailed human-readable explanations …

Recently, transfer learning from pre-trained language models has proven to be effective in a
variety of natural language processing tasks, including sentiment analysis. This paper aims …

We present RuDSI, a new benchmark for word sense induction (WSI) in Russian. The
dataset was created using manual annotation and semi-automatic clustering of Word Usage …

The goal of the present study was to investigate the interaction between different senses of
polysemous nouns (metonymies and metaphors) and different meanings of homonyms …

We describe our solutions for semantic frame and role induction subtasks of SemEval 2019
Task 2. Our approaches got the highest scores, and the solution for the frame induction …

SemEval-2020 Task 1 is devoted to detection of changes in word meaning over time. The
first subtask raises a question if a particular word has acquired or lost any of its senses …

We present our system for semantic frame induction that showed the best performance in
Subtask B. 1 and finished as the runner-up in Subtask A of the SemEval 2019 Task 2 on …

We present a sense-annotated corpus for Russian. The resource was obtained my manually
annotating texts from the OpenCorpora corpus, an open corpus for the Russian language …

In this paper, we present Watasense, an unsupervised system for word sense
disambiguation. Given a sentence, the system chooses the most relevant sense of each …