Search Results for author:
Found 8 papers, 6 papers with code
Large Language Models in Targeted Sentiment Analysis
Reasoning capabilities of the fine-tuned Flan-T5 models with THoR achieve at least 5% increment with the base-size model compared to the results of the zero-shot experiment.
nicolay-r at SemEval-2024 Task 3: Using Flan-T5 for Reasoning Emotion Cause in Conversations with Chain-of-Thought on Emotion States
Inspired by the most recent advances in Chain-of-Thought, in this work, we exploit the existing three-hop reasoning approach (THOR) to perform large language model instruction-tuning for answering: emotion states (THOR-state), and emotion caused by one speaker to the other (THOR-cause).
RuSentNE-2023: Evaluating Entity-Oriented Sentiment Analysis on Russian News Texts
The paper describes the RuSentNE-2023 evaluation devoted to targeted sentiment analysis in Russian news texts.
Attention-Based Neural Networks for Sentiment Attitude Extraction using Distant Supervision
In this paper, we provide a study on attention-based context encoders in the sentiment attitude extraction task.
Studying Attention Models in Sentiment Attitude Extraction Task
In this paper, we provide a study on attention-based context encoders in the sentiment attitude extraction task.
Sentiment Frames for Attitude Extraction in Russian
Texts can convey several types of inter-related information concerning opinions and attitudes.
Distant Supervision for Sentiment Attitude Extraction
News articles often convey attitudes between the mentioned subjects, which is essential for understanding the described situation.
Extracting Sentiment Attitudes From Analytical Texts
In this paper we present the RuSentRel corpus including analytical texts in the sphere of international relations.
