Anastasia Kotelnikova

Large-scale pre-trained language models have demonstrated impressive results in producing human-like texts. However, controlling the text generation process remains a challenge for researchers. Controllable text generation consists of generating sentences that satisfy desired constraints (e.g., sentiment, topic, or keywords). Recent studies that co...

Controllable story generation towards keywords or key phrases is one of the purposes of using language models. Recent work has shown that various decoding strategies prove to be effective in achieving a high level of language control. Such strategies require less computational resources compared to approaches based on fine-tuning pre-trained langua...

Significant progress has been made in sentiment analysis over the past few years, especially due to the application of deep neural language models. However, there is a problem of transferability of trained models from one domain to another, especially for less studied languages such as Russian. We propose an approach to build cross-domain sentiment...

The performance of sentiment analysis methods has greatly increased in recent years. This is due to the use of various models based on the Transformer architecture, in particular BERT. However, deep neural network models are difficult to train and poorly interpretable. An alternative approach is rule-based methods using sentiment lexicons. They are...

The performance of sentiment analysis methods has greatly increased in recent years. This is due to the use of various models based on the Transformer architecture, in particular BERT. However, deep neural network models are difficult to train and poorly interpretable. An alternative approach is rule-based methods using sentiment lexicons. They are...

Рассматриваются способы создания словарей оценочной лексики на русском и английском языках с указанием их достоинств и недостатков. Анализируются 13 русскоязычных и 19 англоязычных словарей - приводятся их количественные характеристики и способы создания, вычисляются объединения и пересечения, определяется общая лексика, исследуется распределение п...

Most sentiment lexicons include individual words rather than collocations. However, the use of collocations can improve the performance of sentiment analysis since the meaning of some collocations cannot be derived from the meaning of their constituents, for example, “ ” (“kick the bucket”) or “ ” (“it is impossible to take one’s eyes off something...