Results 101 to 110 of about 15,771 (225)

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

SemEval-2007 task 15 [PDF]

Proceedings of the 4th International Workshop on Semantic Evaluations - SemEval '07, 2007
The TempEval task proposes a simple way to evaluate automatic extraction of temporal relations. It avoids the pitfalls of evaluating a graph of inter-related labels by defining three sub tasks that allow pairwise evaluation of temporal relations. The task not only allows straightforward evaluation, it also avoids the complexities of full temporal ...
Robert Gaizauskas, Graham Katz, Frank Schilder, Mark Hepple, Marc Verhagen, James Pustejovsky   +5 more
openaire   +2 more sources

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, Carla Rodrigues, Regina Sousa, Luis Mendes Gomes   +3 more
wiley   +1 more source

Improving Multi-Label Emotion Classification on Imbalanced Social Media Data With BERT and Clipped Asymmetric Loss

IEEE Access
This research addresses the challenge of multi-label emotion classification on imbalanced datasets using a BERT-based model. Emotion classification, essential for applications like social media analysis and sentiment monitoring, often suffers from class ...
Sandhya Ramakrishnan, L. D. Dhinesh Babu
doaj   +1 more source

ShotgunWSD 2.0: An Improved Algorithm for Global Word Sense Disambiguation

IEEE Access, 2019
ShotgunWSD is a recent unsupervised and knowledge-based algorithm for global word sense disambiguation (WSD). The algorithm is inspired by the Shotgun sequencing technique, which is a broadly-used whole genome sequencing approach. ShotgunWSD performs WSD
Andrei M. Butnaru, Radu Tudor Ionescu
doaj   +1 more source

Revisiting Recurrent Networks for Paraphrastic Sentence Embeddings

, 2017
We consider the problem of learning general-purpose, paraphrastic sentence embeddings, revisiting the setting of Wieting et al. (2016b). While they found LSTM recurrent networks to underperform word averaging, we present several developments that ...
Gimpel, Kevin, Wieting, John
core   +1 more source

SemEval-2016 Task 2: Interpretable Semantic Textual Similarity [PDF]

Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 2016
Comunicació presentada al 10th International Workshop on Semantic Evaluation (SemEval-2016), celebrat els dies 16 i 17 de juny de 2016 a San Diego, Califòrnia.
Agirre, Eneko, Gonzalez Agirre, Aitor, Lopez-Gazpio, Iñigo, Maritxalar, Montserrat, Rigau Claramunt, German, Uria, Larraitz   +5 more
openaire   +3 more sources

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, Muhammad Babar, Syed Zafar Ali Shah, Ali Abbas, Muhammad Sohail Hayat, Shafiq Ahmad, Sadaqat Jan, Rahul Priyadarshi   +7 more
wiley   +1 more source

AutoSense Model for Word Sense Induction

, 2018
Word sense induction (WSI), or the task of automatically discovering multiple senses or meanings of a word, has three main challenges: domain adaptability, novel sense detection, and sense granularity flexibility. While current latent variable models are
Amplayo, Reinald Kim, Hwang, Seung-won, Song, Min   +2 more
core   +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, Marton Gergely, Khaled Shuaib, Mahmaod Alrawad   +3 more
wiley   +1 more source