Results 101 to 110 of about 15,951 (231)

Modern Approaches to Aspect-Based Sentiment Analysis

Труды Института системного программирования РАН, 2018
The paper presents a survey of methods solving the actual task of aspect-based sentiment analysis. Solutions for this task were proposed at multiple natural language processing conferences.
I. . Andrianov, V. . Mayorov, D. . Turdakov   +2 more
doaj   +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

SemEval-2016 Task 3: Community Question Answering [PDF]

Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 2016
This paper describes the SemEval--2016 Task 3 on Community Question Answering, which we offered in English and Arabic. For English, we had three subtasks: Question--Comment Similarity (subtask A), Question--Question Similarity (B), and Question--External Comment Similarity (C).
Nakov, Preslav, Màrquez, Lluís, Moschitti, Alessandro, Magdy, Walid, Mubarak, Hamdy, Freihat, Abed Alhakim, Glass, James, Randeree, Bilal   +7 more
openaire   +3 more sources

SemEval-2023 Task 11: Learning with Disagreements (LeWiDi)

Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023), 2023
NLP datasets annotated with human judgments are rife with disagreements between the judges. This is especially true for tasks depending on subjective judgments such as sentiment analysis or offensive language detection. Particularly in these latter cases, the NLP community has come to realize that the approach of 'reconciling' these different ...
Leonardelli, Elisa, Uma, Alexandra, Abercrombie, Gavin, Almanea, Dina, Basile, Valerio, Fornaciari, Tommaso, Plank, Barbara, Rieser, Verena, Poesio, Massimo   +8 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

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

SemEval-2016 Task 4: Sentiment Analysis in Twitter [PDF]

Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 2016
This paper discusses the fourth year of the ``Sentiment Analysis in Twitter Task''. SemEval-2016 Task 4 comprises five subtasks, three of which represent a significant departure from previous editions. The first two subtasks are reruns from prior years and ask to predict the overall sentiment, and the sentiment towards a topic in a tweet. The three new
Nakov P, Ritter A, Rosenthal S, Sebastiani F, Stoyanov V   +4 more
openaire   +4 more sources

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, Iftikhar Ahmed, Khwaja Naveed, Malika Bendechache, Pramita Mishra   +4 more
wiley   +1 more source