Zobrazeno 1 - 10
of 10 644
pro vyhledávání: '"Kolesnikova In"'
Autor:
Alonso, Elena R., Insausti, Aran, Kolesniková, Lucie, León, Iker, McGuire, Brett A., Shingledecker, Christopher N., Agúndez, Marcelino, Cernicharo, José, Rivilla, Víctor M., Cabezas, Carlos
Publikováno v:
Elena R. Alonso et al 2024 ApJ 976 95
This work aims to spectroscopically characterize and provide for the first time direct experimental frequencies of the ground vibrational state and two excited states of the simplest alkynyl thiocyanate (HCCSCN) for astrophysical use. Both microwave
Externí odkaz:
http://arxiv.org/abs/2411.11802
Stress is a common feeling in daily life, but it can affect mental well-being in some situations, the development of robust detection models is imperative. This study introduces a methodical approach to the stress identification in code-mixed texts f
Externí odkaz:
http://arxiv.org/abs/2410.06428
Autor:
Yigezu, Mesay Gemeda, Mersha, Melkamu Abay, Bade, Girma Yohannis, Kalita, Jugal, Kolesnikova, Olga, Gelbukh, Alexander
Publikováno v:
ACLing 2024: 6th International Conference on AI in Computational Linguistics
The proliferation of fake news has emerged as a significant threat to the integrity of information dissemination, particularly on social media platforms. Misinformation can spread quickly due to the ease of creating and disseminating content, affecti
Externí odkaz:
http://arxiv.org/abs/2410.02609
This study performs analysis of Predictive statements, Hope speech, and Regret Detection behaviors within cryptocurrency-related discussions, leveraging advanced natural language processing techniques. We introduce a novel classification scheme named
Externí odkaz:
http://arxiv.org/abs/2409.02836
This study delves into the relationship between emotional trends from X platform data and the market dynamics of well-known cryptocurrencies Cardano, Binance, Fantom, Matic, and Ripple over the period from October 2022 to March 2023. Leveraging Senti
Externí odkaz:
http://arxiv.org/abs/2405.03084
Autor:
Tonja, Atnafu Lambebo, Balouchzahi, Fazlourrahman, Butt, Sabur, Kolesnikova, Olga, Ceballos, Hector, Gelbukh, Alexander, Solorio, Thamar
The paper focuses on the marginalization of indigenous language communities in the face of rapid technological advancements. We highlight the cultural richness of these languages and the risk they face of being overlooked in the realm of Natural Lang
Externí odkaz:
http://arxiv.org/abs/2404.05365
Recent research in natural language processing (NLP) has achieved impressive performance in tasks such as machine translation (MT), news classification, and question-answering in high-resource languages. However, the performance of MT leaves much to
Externí odkaz:
http://arxiv.org/abs/2403.19365
Autor:
Tonja, Atnafu Lambebo, Azime, Israel Abebe, Belay, Tadesse Destaw, Yigezu, Mesay Gemeda, Mehamed, Moges Ahmed, Ayele, Abinew Ali, Jibril, Ebrahim Chekol, Woldeyohannis, Michael Melese, Kolesnikova, Olga, Slusallek, Philipp, Klakow, Dietrich, Xiong, Shengwu, Yimam, Seid Muhie
Large language models (LLMs) have gained popularity recently due to their outstanding performance in various downstream Natural Language Processing (NLP) tasks. However, low-resource languages are still lagging behind current state-of-the-art (SOTA)
Externí odkaz:
http://arxiv.org/abs/2403.13737
This paper presents the creation of initial bilingual corpora for thirteen very low-resource languages of India, all from Northeast India. It also presents the results of initial translation efforts in these languages. It creates the first-ever paral
Externí odkaz:
http://arxiv.org/abs/2312.04764
SpaDeLeF: A Dataset for Hierarchical Classification of Lexical Functions for Collocations in Spanish
In natural language processing (NLP), lexical function is a concept to unambiguously represent semantic and syntactic features of words and phrases in text first crafted in the Meaning-Text Theory. Hierarchical classification of lexical functions inv
Externí odkaz:
http://arxiv.org/abs/2311.04189