OffensEval
Related Papers
Amobee at SemEval-2019 Tasks 5 and 6: Multiple Choice CNN Over Contextual Embedding
AbstractThis article describes Amobee’s participation in “HatEval: Multilingual detection of hate speech against immigrants and women in Twitter” (task 5) and “OffensEval: Identifying and Categorizing Offensive Language in Social Media” …...
Duluth at SemEval-2019 Task 6: Lexical Approaches to Identify and Categorize Offensive Tweets
AbstractThis paper describes the Duluth systems that participated in SemEval–2019 Task 6, Identifying and Categorizing Offensive Language in Social Media (OffensEval). For the most part these systems took traditional Machine …...
Embeddia at SemEval-2019 Task 6: Detecting Hate with Neural Network and Transfer Learning Approaches
AbstractSemEval 2019 Task 6 was OffensEval: Identifying and Categorizing Offensive Language in Social Media. The task was further divided into three sub-tasks: offensive language identification, automatic categorization of offense types, …...
Emoji Powered Capsule Network to Detect Type and Target of Offensive Posts in Social Media
AbstractThis paper describes a novel research approach to detect type and target of offensive posts in social media using a capsule network. The input to the network was character embeddings …...
Ghmerti at SemEval-2019 Task 6: A Deep Word- and Character-based Approach to Offensive Language Identification
AbstractThis paper presents the models submitted by Ghmerti team for subtasks A and B of the OffensEval shared task at SemEval 2019. OffensEval addresses the problem of identifying and categorizing …...
HAD-Tübingen at SemEval-2019 Task 6: Deep Learning Analysis of Offensive Language on Twitter: Identification and Categorization
AbstractThis paper describes the submissions of our team, HAD-Tübingen, for the SemEval 2019 - Task 6: “OffensEval: Identifying and Categorizing Offensive Language in Social Media”. We participated in all the …...
INGEOTEC at SemEval-2019 Task 5 and Task 6: A Genetic Programming Approach for Text Classification
AbstractThis paper describes our participation in HatEval and OffensEval challenges for English and Spanish languages. We used several approaches, B4MSA, FastText, and EvoMSA. Best results were achieved with EvoMSA, which …...
JCTICOL at SemEval-2019 Task 6: Classifying Offensive Language in Social Media using Deep Learning Methods, Word/Character N-gram Features, and Preprocessing Methods
AbstractIn this paper, we describe our submissions to SemEval-2019 task 6 contest. We tackled all three sub-tasks in this task “OffensEval - Identifying and Categorizing Offensive Language in Social Media”. …...
KMI-Coling at SemEval-2019 Task 6: Exploring N-grams for Offensive Language detection
AbstractIn this paper, we present the system description of Offensive language detection tool which is developed by the KMI_Coling under the OffensEval Shared task. The OffensEval Shared Task was conducted …...
Stop PropagHate at SemEval-2019 Tasks 5 and 6: Are abusive language classification results reproducible?
AbstractThis paper summarizes the participation of Stop PropagHate team at SemEval 2019. Our approach is based on replicating one of the most relevant works on the literature, using word embeddings …...
UTFPR at SemEval-2019 Task 6: Relying on Compositionality to Find Offense
AbstractWe present the UTFPR system for the OffensEval shared task of SemEval 2019: A character-to-word-to-sentence compositional RNN model trained exclusively over the training data provided by the organizers. We find …...
Zeyad at SemEval-2019 Task 6: That’s Offensive! An All-Out Search For An Ensemble To Identify And Categorize Offense in Tweets.
AbstractThe objective of this paper is to provide a description for a classification system built for SemEval-2019 Task 6: OffensEval. This system classifies a tweet as either offensive or not …...
CyberTronics at SemEval-2020 Task 12: Multilingual Offensive Language Identification over Social Media
AbstractThe SemEval-2020 Task 12 (OffensEval) challenge focuses on detection of signs of offensiveness using posts or comments over social media. This task has been organized for several languages, e.g., Arabic, …...
I2C at SemEval-2020 Task 12: Simple but Effective Approaches to Offensive Speech Detection in Twitter
AbstractThis paper describes the systems developed for I2C Group to participate on Subtasks A and B in English, and Subtask A in Turkish and Arabic in OffensEval (Task 12 of …...
NLP_Passau at SemEval-2020 Task 12: Multilingual Neural Network for Offensive Language Detection in English, Danish and Turkish
AbstractThis paper describes a neural network (NN) model that was used for participating in the OffensEval, Task 12 of the SemEval 2020 workshop. The aim of this task is to …...
UJNLP at SemEval-2020 Task 12: Detecting Offensive Language Using Bidirectional Transformers
AbstractIn this paper, we built several pre-trained models to participate SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media. In the common task of Offensive Language Identification in Social …...
Voice@SRIB at SemEval-2020 Tasks 9 and 12: Stacked Ensemblingmethod for Sentiment and Offensiveness detection in Social Media
AbstractIn social-media platforms such as Twitter, Facebook, and Reddit, people prefer to use code-mixed language such as Spanish-English, Hindi-English to express their opinions. In this paper, we describe different models …...