Results 141 to 150 of about 21,648 (265)
SemEval-2019 Task 4: Hyperpartisan News Detection
International Workshop on Semantic Evaluation, 2019 Hyperpartisan news is news that takes an extreme left-wing or right-wing standpoint. If one is able to reliably compute this meta information, news articles may be automatically tagged, this way encouraging or discouraging readers to consume the text. It
Johannes Kiesel +7 more
semanticscholar +1 more source
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 ...
Steven Bethard +3 more
openaire +1 more source
Fast Fine‐Tuning Large Language Models for Aspect‐Based Sentiment Analysis
Electronics Letters, Volume 61, Issue 1, January/December 2025.The method proposed in this study aims to reduce the execution time required for fine‐tuning large language models in aspect‐based sentiment analysis. To achieve efficient fine‐tuning, the large‐language model parameter tuning for new data is accelerated through rank decomposition.
Chaelyn Lee, Jaesung Lee
wiley +1 more source
Drug–drug interaction extraction‐based system: An natural language processing approach
Expert Systems, Volume 42, Issue 1, January 2025.Abstract Poly‐medicated patients, especially those over 65, have increased. Multiple drug use and inappropriate prescribing increase drug–drug interactions, adverse drug reactions, morbidity, and mortality. This issue was addressed with recommendation systems.
José Machado +3 more
wiley +1 more source
Learning for clinical named entity recognition without manual annotations
Informatics in Medicine Unlocked, 2018 Background: Named entity recognition (NER) systems are commonly built using supervised methods that use machine learning to learn from corpora manually annotated with named entities.
Omid Ghiasvand, Rohit J. Kate
doaj +1 more source
BERTastic at SemEval-2023 Task 3: Fine-Tuning Pretrained Multilingual Transformers Does Order Matter? [PDF]
, 2023 Tarek M. Mahmoud, Preslav Nakov
openalex +1 more source
Explainable AI Models for Decoding Emotional Subtexts on Social Media
Complexity, Volume 2025, Issue 1, 2025.Social media platforms, such as X (formerly Twitter), provide users with concise but impactful tools to express their views and feelings. Users present their views and express their feelings in hashtags and emojis on a wide range of topics. The sheer volume of this textual data offers a rich source for analyzing public sentiment and emotions.
Dost Muhammad +4 more
wiley +1 more source
Silp_nlp at SemEval-2023 Task 2: Cross-lingual Knowledge Transfer for Mono-lingual Learning [PDF]
, 2023 Sumit Kumar Singh, Uma Tiwary
openalex +1 more source
Sarcasm Detection in Sentiment Analysis Using Recurrent Neural Networks
International Journal of Distributed Sensor Networks, Volume 2025, Issue 1, 2025.In recent years, online opinionated textual data volume has surged, necessitating automated analysis to extract valuable insights. Data mining and sentiment analysis have become essential for analysing this type of text. Sentiment analysis is a text classification problem associated with many challenges, including better data preprocessing and sarcasm ...
Maneeha Rani +7 more
wiley +1 more source
Exploring NLP‐Based Solutions to Social Media Moderation Challenges
Human Behavior and Emerging Technologies, Volume 2025, Issue 1, 2025.The rise of social media has revolutionized global communication, enabling users and businesses to connect, advertise, and monitor competitors. However, this expansion has also fueled toxic behaviors like hate speech and harassment, exposing innocent users to harmful content while overwhelming human moderators and impacting their well‐being. To address
Heba Saleous +3 more
wiley +1 more source