Results 101 to 110 of about 16,304 (224)
Integrating Weakly Supervised Word Sense Disambiguation into Neural Machine Translation
, 2018 This paper demonstrates that word sense disambiguation (WSD) can improve neural machine translation (NMT) by widening the source context considered when modeling the senses of potentially ambiguous words.
Henderson, James +3 more
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 +5 more
openaire +2 more sources
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
Robust Incremental Neural Semantic Graph Parsing
, 2017 Parsing sentences to linguistically-expressive semantic representations is a key goal of Natural Language Processing. Yet statistical parsing has focused almost exclusively on bilexical dependencies or domain-specific logical forms.
Blunsom, Phil, Buys, Jan
core +1 more source
R2D2 at SemEval-2022 Task 6: Are language models sarcastic enough? Finetuning pre-trained language models to identify sarcasm [PDF]
, 2022 Mayukh Sharma +2 more
openalex +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
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
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
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
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