6 citations · 6 across the 2 of their papers we have counts for
14 papers · 1 filter
Multilingual Sentiment Aware Text Summarization A Reinforcement Learning Approach for Consistency Maintenance
Mikhail Krasitskii, Alexander Gelbukh, Olga Kolesnikova +1
Reinforcement Learning from Human Feedback (RLHF) has significantly improved the quality and fluency of large language models in text summarization. However, its impact on affectiv…
Irony Detection in Urdu Text: A Comparative Study Using Machine Learning Models and Large Language Models
Fiaz Ahmad, Nisar Hussain, Amna Qasim +3
Ironic identification is a challenging task in Natural Language Processing, particularly when dealing with languages that differ in syntax and cultural context. In this work, we ai…
Bilingual Word Level Language Identification for Omotic Languages
Mesay Gemeda Yigezu, Girma Yohannis Bade, Atnafu Lambebo Tonja +3
Language identification is the task of determining the languages for a given text. In many real world scenarios, text may contain more than one language, particularly in multilingu…
Hybrid Extractive Abstractive Summarization for Multilingual Sentiment Analysis
Mikhail Krasitskii, Grigori Sidorov, Olga Kolesnikova +2
We propose a hybrid approach for multilingual sentiment analysis that combines extractive and abstractive summarization to address the limitations of standalone methods. The model…
Multilingual Sentiment Analysis of Summarized Texts: A Cross-Language Study of Text Shortening Effects
Mikhail Krasitskii, Grigori Sidorov, Olga Kolesnikova +2
Summarization significantly impacts sentiment analysis across languages with diverse morphologies. This study examines extractive and abstractive summarization effects on sentiment…
Advancing Sentiment Analysis in Tamil-English Code-Mixed Texts: Challenges and Transformer-Based Solutions
Mikhail Krasitskii, Olga Kolesnikova, Liliana Chanona Hernandez +2
The sentiment analysis task in Tamil-English code-mixed texts has been explored using advanced transformer-based models. Challenges from grammatical inconsistencies, orthographic v…