SemEval-2023 Task 1: Visual Word Sense Disambiguation [PDF]
, 2023 Alessandro Raganato +4 more
openalex +1 more source
SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News [PDF]
, 2017 Keith Cortis +6 more
openalex +1 more source
Jack-Ryder at SemEval-2023 Task 5: Zero-Shot Clickbait Spoiling by Rephrasing Titles as Questions [PDF]
, 2023 Dirk Wangsadirdja +3 more
openalex +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
A Hybrid Deep Implicit Neural Model for Sentiment Analysis via Transfer Learning
IEEE AccessWe present a neural model for sentiment analysis of social network texts with a special focus on cryptocurrency-related content using deep transfer learning. A challenge of deep learning is its need for abundant data.
Kia Jahanbin, Mohammad Ali Zare Chahooki
doaj +1 more source
UMDuluth-CS8761 at SemEval-2018 Task 2: Emojis: Too many Choices? [PDF]
, 2018 Jonathan Beaulieu, Dennis Asamoah Owusu
openalex +1 more source
IITK@LCP at SemEval-2021 Task 1: Classification for Lexical Complexity Regression Task [PDF]
, 2021 Neil Rajiv Shirude +4 more
openalex +1 more source
HCTI at SemEval-2017 Task 1: Use convolutional neural network to evaluate Semantic Textual Similarity [PDF]
, 2017 Yang Shao
openalex +1 more source
Proceedings of the 4th International Workshop on Semantic Evaluations - SemEval '07, 2007 Collin Baker +2 more
openaire +1 more source
On SemEval-2010 Japanese WSD Task
Journal of Natural Language Processing, 2011 An overview of the SemEval-2 Japanese WSD task is presented. The new characteristics of our task are (1) the task will use the first balanced Japanese sense-tagged corpus, and (2) the task will take into account not only the instances that have a sense in the given set but also the instances that have a sense that cannot be found in the set.
Manabu Okumura +3 more
openaire +2 more sources