Results 241 to 250 of about 21,648 (265)

Comparison between parameter-efficient techniques and full fine-tuning: A case study on multilingual news article classification. [PDF]

PLoS One
Razuvayevskaya O, Wu B, Leite JA, Heppell F, Srba I, Scarton C, Bontcheva K, Song X.   +7 more
europepmc   +1 more source

An innovative 3D attention mechanism for multi-label emotion classification. [PDF]

Sci Rep
Luo H, Shao T, Li S, Kishi T.
europepmc   +1 more source

TEGAA: transformer-enhanced graph aspect analyzer with semantic contrastive learning for implicit aspect detection. [PDF]

Front Artif Intell
Soni PK, Rambola R.
europepmc   +1 more source

TASCI: transformers for aspect-based sentiment analysis with contextual intent integration. [PDF]

PeerJ Comput Sci
Chaudhry HN, Kulsoom F, Ullah Khan Z, Aman M, Khan SU, Albanyan A.   +5 more
europepmc   +1 more source

Feature extraction from customer reviews using enhanced rules. [PDF]

PeerJ Comput Sci
Santhiran R, Varathan KD, Chiam YK.
europepmc   +1 more source

ArSa-Tweets: A novel Arabic sarcasm detection system based on deep learning model. [PDF]

Heliyon
Abuein Q, Al-Khatib RM, Migdady A, Jawarneh MS, Al-Khateeb A.   +4 more
europepmc   +1 more source

A review of reinforcement learning for natural language processing and applications in healthcare. [PDF]

J Am Med Inform Assoc
Liu Y, Wang H, Zhou H, Li M, Hou Y, Zhou S, Wang F, Hoetzlein R, Zhang R.   +8 more
europepmc   +1 more source

SemEval 2015, Task 7: Diachronic Text Evaluation [PDF]

Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), 2015
In this paper we describe a novel task, namely the Diachronic Text Evaluation task. A corpus of snippets which contain relevant information for the time when the text was created is extracted from a large collection of newspapers published between 1700 and 2010.
Popescu Octavian, Strapparava Carlo
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SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval

arXiv.org
The rapid spread of online disinformation presents a global challenge, and machine learning has been widely explored as a potential solution. However, multilingual settings and low-resource languages are often neglected in this field. To address this gap,
Qiwei Peng, Róbert Móro, Michal Gregor, Ivan Srba, Simon Ostermann, Marián Simko, Juraj Podrouvzek, Mat'uvs Mesarvc'ik, Jaroslav Kopvcan, Anders Sogaard   +9 more
semanticscholar   +1 more source