BERTweet: A pre-trained language model for English Tweets (EMNLP-2020)
-
Updated
Jul 22, 2024 - Python
BERTweet: A pre-trained language model for English Tweets (EMNLP-2020)
Deep-learning models of NTUA-SLP team submitted in SemEval 2018 tasks 1, 2 and 3.
MirasText
A Simple and Accurate Neural Network Model for Irony Detection in Twitter
Datasets used for iSarcasmEval shared-task (Task 6 at SemEval 2022)
Code and data used for participation in SemEval-2018 Task 3: "Irony detection in English tweets"
System for irony detection in product reviews
This repo represents model developed for Irony and sentiment detection in Arabic tweets in WANLP shared tasks on sarcasm and sentiment detection in Arabic tweets
Irony Detection in a Multilingual Context
Code for 3 papers: 1) "Fuzzy-Rough Nearest Neighbour Approaches for Emotion Detection in Tweets"; 2) "LT3 at SemEval-2022 Task 6: Fuzzy-Rough Nearest neighbor Classification for Sarcasm Detection"; 3) "Fuzzy Rough Nearest Neighbour Methods for Detecting Emotions, Hate Speech and Irony" by O. Kaminska, Ch. Cornelis and V. Hoste.
Course Project (ELEC 880 @ Queen's University)
Paper: A Cancel Culture Corpus through the lens of Natural Language Processing
This repo contains work carried out for SemEval 2022 Task 6: iSarcasmEval: Intended Sarcasm Detection In English and Arabic
Code used in experiment for CLEF2022's Shared Task: Profiling Irony and Stereotype Spreaders on Twitter (IROSTEREO)
This repo is the work done for IDAT 2019 Shared Task — Shared Task on detecting irony in Arabic tweets by RGCL
Natural Language Processing - Sarcasm Detection
Persian Irony Detection, include a Persian dataset, creating a dataset automatically, and finetuning transformer-based language models for the task
This is the Github repository for SemEval-2018 Task 3
Multi-View Sentiment Corpus (EACL 2017): tweets labelled by three annotators with sentiment, emotion, irony, subjectivity and implicitness
Add a description, image, and links to the irony-detection topic page so that developers can more easily learn about it.
To associate your repository with the irony-detection topic, visit your repo's landing page and select "manage topics."