Results 71 to 80 of about 15,771 (225)

SemEval-2017 Task 12: Clinical TempEval [PDF]

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), 2017
Clinical TempEval 2017 aimed to answer the question: how well do systems trained on annotated timelines for one medical condition (colon cancer) perform in predicting timelines on another medical condition (brain cancer)? Nine sub-tasks were included, covering problems in time expression identification, event expression identification and temporal ...
Guergana Savova, James Pustejovsky, Martha Palmer, Steven Bethard   +3 more
openaire   +2 more sources

Classifier Performance on Long‐Tail Distributions

Statistical Analysis and Data Mining: An ASA Data Science Journal, Volume 18, Issue 4, August 2025.
ABSTRACT This paper introduces a Gaussian mixture model designed to explore the implications of long‐tailedness on classification performance. Our study reveals that simple under‐specified classifiers are inherently limited in reducing generalization error within this framework, a limitation overcome by well‐specified and even more complex over ...
Artur Pak, Maxat Tezekbayev, Arman Bolatov, Igor Melnykov, Kaisar Dauletbek, Zhenisbek Assylbekov   +5 more
wiley   +1 more source

Multi-task transfer learning for the prediction of entity modifiers in clinical text: application to opioid use disorder case detection

Journal of Biomedical Semantics
Background The semantics of entities extracted from a clinical text can be dramatically altered by modifiers, including entity negation, uncertainty, conditionality, severity, and subject.
Abdullateef I. Almudaifer, Whitney Covington, JaMor Hairston, Zachary Deitch, Ankit Anand, Caleb M. Carroll, Estera Crisan, William Bradford, Lauren A. Walter, Ellen F. Eaton, Sue S. Feldman, John D. Osborne   +11 more
doaj   +1 more source

Aspect-Based Sentiment Analysis of Twitter Influencers to Predict the Trend of Cryptocurrencies Based on Hybrid Deep Transfer Learning Models

IEEE Access, 2023
With the expansion of social networks, sentiment analysis has become one of the hot topics in machine learning. However, in traditional sentiment analysis, the text is considered of a general nature and ignores the different aspects that may exist in the
Kia Jahanbin, Mohammad Ali Zare Chahooki
doaj   +1 more source

Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets

, 2017
There has been a good amount of progress in sentiment analysis over the past 10 years, including the proposal of new methods and the creation of benchmark datasets.
Barnes, Jeremy, Klinger, Roman, Walde, Sabine Schulte im   +2 more
core   +1 more source

CTSys at SemEval-2018 Task 3: Irony in Tweets [PDF]

Proceedings of The 12th International Workshop on Semantic Evaluation, 2018
L'objectif de cet article est de fournir une description d'un système construit comme notre participation à la tâche 3 de SemEval-2018 sur la détection de l'ironie dans les tweets en anglais. Ce système classe un tweet comme ironique ou non ironique grâce à une approche d'apprentissage supervisé.
Myan Sherif, Sherine Mamdouh, Wegdan Ghazi   +2 more
openaire   +2 more sources

Joint Extraction Method for Spatial Relations in Chinese Geological Texts

Earth and Space Science, Volume 12, Issue 8, August 2025.
Abstract Extracting spatial relations of geological entities is an important prerequisite for achieving natural language processing tasks such as geological knowledge question answering and semantic search, and is an important means to achieve structural reconstruction of unstructured geological data.
Chuan Chen, Xiuguo Liu, Weihua Hua, Xinling Zeng, Zihan Cao, Peng Li, Chunyu Qi, Yu Huang, Jiameng Chen, Wencheng Wei   +9 more
wiley   +1 more source

Enhanced Sentiment Intensity Regression Through LoRA Fine-Tuning on Llama 3

IEEE Access
Sentiment analysis and emotion detection are critical research areas in natural language processing (NLP), offering benefits to numerous downstream tasks.
Diefan Lin, Yi Wen, Weishi Wang, Yan Su
doaj   +1 more source

ADeCNN: An Improved Model for Aspect-Level Sentiment Analysis Based on Deformable CNN and Attention

IEEE Access, 2020
Aspect-level sentiment analysis aims at identifying the sentiment polarity of target in the context. In most of the previous sentiment analysis models, there usually exists the problem of insufficient extraction capability of local features and long ...
Jie Zhou, Siqi Jin, Xinli Huang
doaj   +1 more source

Toxic Discourse in the Digital Battlefield: Analysing Telegram Channels During the Russia–Ukraine ‘Conflict’

Expert Systems, Volume 42, Issue 7, July 2025.
ABSTRACT Instant messenger Telegram has emerged as a favoured platform for far‐right activism, conspiracy theories, political propaganda, and misinformation, which has its own target audience. This study explores the application of multilingual pre‐trained language models to detect and measure toxicity in political content on Telegram channels.
Arsenii Tretiakov, Sergio D'Antonio‐Maceiras, Áurea Anguera de Sojo Hernández, Alejandro Martín   +3 more
wiley   +1 more source