pwc-archive/papers-with-abstracts huggingface dataset:
INGEOTEC at SemEval-2019 Task 5 and Task 6: A Genetic Programming Approach for Text Classification
Authors: Mario Graff, Mir, Sabino a-Jim{\'e}nez, Eric Tellez, Daniela Alej Ochoa, ra
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: General Classification, text-classification, Text Classification

This 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 is a multilingual and domain-independent architecture that combines the prediction of different knowledge sources to solve text classification problems.
JCTICOL at SemEval-2019 Task 6: Classifying Offensive Language in Social Media using Deep Learning Methods, Word/Character N-gram Features, and Preprocessing Methods
Authors: Yaakov HaCohen-Kerner, Ziv Ben-David, Gal Didi, Eli Cahn, Shalom Rochman, Elyashiv Shayovitz
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Position

In 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{''}. In our system called JCTICOL (Jerusalem College of Technology Identifies and Categorizes Offensive Language), we applied various supervised ML methods. We applied various combinations of word/character n-gram features using the TF-IDF scheme. In addition, we applied various combinations of seven basic preprocessing methods. Our best submission, an RNN model was ranked at the 25th position out of 65 submissions for the most complex sub-task (C).
jhan014 at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media
Authors: Jiahui Han, Shengtan Wu, Xinyu Liu
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Sentence

In this paper, we present two methods to identify and categorize the offensive language in Twitter. In the first method, we establish a probabilistic model to evaluate the sentence offensiveness level and target level according to different sub-tasks. In the second method, we develop a deep neural network consisting of bidirectional recurrent layers with Gated Recurrent Unit (GRU) cells and fully connected layers. In the comparison of two methods, we find both method has its own advantages and drawbacks while they have similar accuracy.
JTML at SemEval-2019 Task 6: Offensive Tweets Identification using Convolutional Neural Networks
Authors: Johnny Torres, Carmen Vaca
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: regression

In this paper, we propose the use of a Convolutional Neural Network (CNN) to identify offensive tweets, as well as the type and target of the offense. We use an end-to-end model (i.e., no preprocessing) and fine-tune pre-trained embeddings (FastText) during training for learning words{'} representation. We compare the proposed CNN model to a baseline model, such as Linear Regression, and several neural models. The results show that CNN outperforms other models, and stands as a simple but strong baseline in comparison to other systems submitted to the Shared Task.
JU\_ETCE\_17\_21 at SemEval-2019 Task 6: Efficient Machine Learning and Neural Network Approaches for Identifying and Categorizing Offensive Language in Tweets
Authors: Preeti Mukherjee, Mainak Pal, Somnath Banerjee, Sudip Kumar Naskar
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Language Identification, Word Embeddings

This paper describes our system submissions as part of our participation (team name: JU{\_}ETCE{\_}17{\_}21) in the SemEval 2019 shared task 6: {``}OffensEval: Identifying and Catego- rizing Offensive Language in Social Media{''}. We participated in all the three sub-tasks: i) Sub-task A: offensive language identification, ii) Sub-task B: automatic categorization of of- fense types, and iii) Sub-task C: offense target identification. We employed machine learn- ing as well as deep learning approaches for the sub-tasks. We employed Convolutional Neural Network (CNN) and Recursive Neu- ral Network (RNN) Long Short-Term Memory (LSTM) with pre-trained word embeddings. We used both word2vec and Glove pre-trained word embeddings. We obtained the best F1- score using CNN based model for sub-task A, LSTM based model for sub-task B and Lo- gistic Regression based model for sub-task C. Our best submissions achieved 0.7844, 0.5459 and 0.48 F1-scores for sub-task A, sub-task B and sub-task C respectively.
KMI-Coling at SemEval-2019 Task 6: Exploring N-grams for Offensive Language detection
Authors: Priya Rani, Atul Kr. Ojha
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6

In 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 in SemEval 2019 workshop. To develop the system, we have explored n-grams up to 8-gram and trained three different namely A, B and C systems for three different subtasks within the OffensEval task which achieves 79.76{\%}, 87.91{\%} and 44.37{\%} accuracy respectively. The task was completed using the dataset provided to us by the OffensEval organisers was the part of OLID dataset. It consists of 13,240 tweets extracted from twitter and were annotated at three levels using crowdsourcing.
LaSTUS/TALN at SemEval-2019 Task 6: Identification and Categorization of Offensive Language in Social Media with Attention-based Bi-LSTM model
Authors: Lutfiye Seda Mut Altin, {\`A}lex Bravo Serrano, Horacio Saggion
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Word Embeddings

We present a bidirectional Long-Short Term Memory network for identifying offensive language in Twitter. Our system has been developed in the context of the SemEval 2019 Task 6 which comprises three different sub-tasks, namely A: Offensive Language Detection, B: Categorization of Offensive Language, C: Offensive Language Target Identification. We used a pre-trained Word Embeddings in tweet data, including information about emojis and hashtags. Our approach achieves good performance in the three sub-tasks.
LTL-UDE at SemEval-2019 Task 6: BERT and Two-Vote Classification for Categorizing Offensiveness
Authors: Piush Aggarwal, Tobias Horsmann, Michael Wojatzki, Torsten Zesch
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: General Classification

We present results for Subtask A and C of SemEval 2019 Shared Task 6. In Subtask A, we experiment with an embedding representation of postings and use BERT to categorize postings. Our best result reaches the 10th place (out of 103). In Subtask C, we applied a two-vote classification approach with minority fallback, which is placed on the 19th rank (out of 65).
Nikolov-Radivchev at SemEval-2019 Task 6: Offensive Tweet Classification with BERT and Ensembles
Authors: Alex Nikolov, Victor Radivchev
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: BIG-bench Machine Learning, General Classification

This paper examines different approaches and models towards offensive tweet classification which were used as a part of the OffensEval 2019 competition. It reviews Tweet preprocessing, techniques for overcoming unbalanced class distribution in the provided test data, and comparison of multiple attempted machine learning models.
NIT\_Agartala\_NLP\_Team at SemEval-2019 Task 6: An Ensemble Approach to Identifying and Categorizing Offensive Language in Twitter Social Media Corpora
Authors: Steve Durairaj Swamy, Anupam Jamatia, Bj{\"o}rn Gamb{\"a}ck, Amitava Das
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6

The paper describes the systems submitted to OffensEval (SemEval 2019, Task 6) on {`}Identifying and Categorizing Offensive Language in Social Media{'} by the {`}NIT{\_}Agartala{\_}NLP{\_}Team{'}. A Twitter annotated dataset of 13,240 English tweets was provided by the task organizers to train the individual models, with the best results obtained using an ensemble model composed of six different classifiers. The ensemble model produced macro-averaged F1-scores of 0.7434, 0.7078 and 0.4853 on Subtasks A, B, and C, respectively. The paper highlights the overall low predictive nature of various linguistic features and surface level count features, as well as the limitations of a traditional machine learning approach when compared to a Deep Learning counterpart.
NLP@UIOWA at SemEval-2019 Task 6: Classifying the Crass using Multi-windowed CNNs
Authors: Jonathan Rusert, Padmini Srinivasan
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Binary Classification, Classification, General Classification

This paper proposes a system for OffensEval (SemEval 2019 Task 6), which calls for a system to classify offensive language into several categories. Our system is a text based CNN, which learns only from the provided training data. Our system achieves 80 - 90{\%} accuracy for the binary classification problems (offensive vs not offensive and targeted vs untargeted) and 63{\%} accuracy for trinary classification (group vs individual vs other).
nlpUP at SemEval-2019 Task 6: A Deep Neural Language Model for Offensive Language Detection
Authors: Jelena Mitrovi{\'c}, Bastian Birkeneder, Michael Granitzer
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Language Modeling, Language Modelling

This paper presents our submission for the SemEval shared task 6, sub-task A on the identification of offensive language. Our proposed model, C-BiGRU, combines a Convolutional Neural Network (CNN) with a bidirectional Recurrent Neural Network (RNN). We utilize word2vec to capture the semantic similarities between words. This composition allows us to extract long term dependencies in tweets and distinguish between offensive and non-offensive tweets. In addition, we evaluate our approach on a different dataset and show that our model is capable of detecting online aggressiveness in both English and German tweets. Our model achieved a macro F1-score of 79.40{\%} on the SemEval dataset.
SINAI at SemEval-2019 Task 6: Incorporating lexicon knowledge into SVM learning to identify and categorize offensive language in social media
Authors: Flor Miriam Plaza-del-Arco, M. Dolores Molina-Gonz{\'a}lez, Maite Martin, L. Alfonso Ure{\~n}a-L{\'o}pez
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: General Classification

Offensive language has an impact across society. The use of social media has aggravated this issue among online users, causing suicides in the worst cases. For this reason, it is important to develop systems capable of identifying and detecting offensive language in text automatically. In this paper, we developed a system to classify offensive tweets as part of our participation in SemEval-2019 Task 6: OffensEval. Our main contribution is the integration of lexical features in the classification using the SVM algorithm.
SSN\_NLP at SemEval-2019 Task 6: Offensive Language Identification in Social Media using Traditional and Deep Machine Learning Approaches
Authors: Thenmozhi D., Senthil Kumar B., Srinethe Sharavanan, Ch, Aravindan rabose
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: BIG-bench Machine Learning, Hate Speech Detection, Language Identification

Offensive language identification (OLI) in user generated text is automatic detection of any profanity, insult, obscenity, racism or vulgarity that degrades an individual or a group. It is helpful for hate speech detection, flame detection and cyber bullying. Due to immense growth of accessibility to social media, OLI helps to avoid abuse and hurts. In this paper, we present deep and traditional machine learning approaches for OLI. In deep learning approach, we have used bi-directional LSTM with different attention mechanisms to build the models and in traditional machine learning, TF-IDF weighting schemes with classifiers namely Multinomial Naive Bayes and Support Vector Machines with Stochastic Gradient Descent optimizer are used for model building. The approaches are evaluated on the OffensEval@SemEval2019 dataset and our team SSN{\_}NLP submitted runs for three tasks of OffensEval shared task. The best runs of SSN{\_}NLP obtained the F1 scores as 0.53, 0.48, 0.3 and the accuracies as 0.63, 0.84 and 0.42 for the tasks A, B and C respectively. Our approaches improved the base line F1 scores by 12{\%}, 26{\%} and 14{\%} for Task A, B and C respectively.
Stop PropagHate at SemEval-2019 Tasks 5 and 6: Are abusive language classification results reproducible?
Authors: Paula Fortuna, Juan Soler-Company, S{\'e}rgio Nunes
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Abusive Language, General Classification, Word Embeddings

This 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 and LSTM. After circumventing some of the problems of the original code, we found poor results when applying it to the HatEval contest (F1=0.45). We think this is due mainly to inconsistencies in the data of this contest. Finally, for the OffensEval the classifier performed well (F1=0.74), proving to have a better performance for offense detection than for hate speech.
TECHSSN at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Tweets using Deep Neural Networks
Authors: Angel Suseelan, Rajalakshmi S, Logesh B, Harshini S, Geetika B, Dyaneswaran S, S Milton Rajendram, Mirnalinee T T
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Classification, General Classification

Task 6 of SemEval 2019 involves identifying and categorizing offensive language in social media. The systems developed by TECHSSN team uses multi-level classification techniques. We have developed two systems. In the first system, the first level of classification is done by a multi-branch 2D CNN classifier with Google{'}s pre-trained Word2Vec embedding and the second level of classification by string matching technique supported by offensive and bad words dictionary. The second system uses a multi-branch 1D CNN classifier with Glove pre-trained embedding layer for the first level of classification and string matching for the second level of classification. Input data with a probability of less than 0.70 in the first level are passed on to the second level. The misclassified examples are classified correctly in the second level.
The Titans at SemEval-2019 Task 6: Offensive Language Identification, Categorization and Target Identification
Authors: Avishek Garain, Arpan Basu
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Language Identification

This system paper is a description of the system submitted to {``}SemEval-2019 Task 6{''}, where we had to detect offensive language in Twitter. There were two specific target audiences, immigrants and women. The language of the tweets was English. We were required to first detect whether a tweet contains offensive content, and then we had to find out whether the tweet was targeted against some individual, group or other entity. Finally we were required to classify the targeted audience.
T\"uKaSt at SemEval-2019 Task 6: Something Old, Something Neu(ral): Traditional and Neural Approaches to Offensive Text Classification
Authors: Madeeswaran Kannan, Lukas Stein
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: General Classification, Sentence, text-classification, Text Classification

We describe our system (T{\"u}KaSt) submitted for Task 6: Offensive Language Classification, at SemEval 2019. We developed multiple SVM classifier models that used sentence-level dense vector representations of tweets enriched with sentiment information and term-weighting. Our best results achieved F1 scores of 0.734, 0.660 and 0.465 in the first, second and third sub-tasks respectively. We also describe a neural network model that was developed in parallel but not used during evaluation due to time constraints.
TUVD team at SemEval-2019 Task 6: Offense Target Identification
Authors: Elena Shushkevich, John Cardiff, Paolo Rosso
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: regression

This article presents our approach for detecting a target of offensive messages in Twitter, including Individual, Group and Others classes. The model we have created is an ensemble of simpler models, including Logistic Regression, Naive Bayes, Support Vector Machine and the interpolation between Logistic Regression and Naive Bayes with 0.25 coefficient of interpolation. The model allows us to achieve 0.547 macro F1-score.
UBC-NLP at SemEval-2019 Task 6: Ensemble Learning of Offensive Content With Enhanced Training Data
Authors: Arun Rajendran, Chiyu Zhang, Muhammad Abdul-Mageed
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Ensemble Learning

We examine learning offensive content on Twitter with limited, imbalanced data. For the purpose, we investigate the utility of using various data enhancement methods with a host of classical ensemble classifiers. Among the 75 participating teams in SemEval-2019 sub-task B, our system ranks 6th (with 0.706 macro F1-score). For sub-task C, among the 65 participating teams, our system ranks 9th (with 0.587 macro F1-score).
UHH-LT at SemEval-2019 Task 6: Supervised vs. Unsupervised Transfer Learning for Offensive Language Detection
Authors: Gregor Wiedemann, Eugen Ruppert, Chris Biemann
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Classification, Clustering, General Classification, Multi-Task Learning, Transfer Learning, Unsupervised Pre-training

We present a neural network based approach of transfer learning for offensive language detection. For our system, we compare two types of knowledge transfer: supervised and unsupervised pre-training. Supervised pre-training of our bidirectional GRU-3-CNN architecture is performed as multi-task learning of parallel training of five different tasks. The selected tasks are supervised classification problems from public NLP resources with some overlap to offensive language such as sentiment detection, emoji classification, and aggressive language classification. Unsupervised transfer learning is performed with a thematic clustering of 40M unlabeled tweets via LDA. Based on this dataset, pre-training is performed by predicting the main topic of a tweet. Results indicate that unsupervised transfer from large datasets performs slightly better than supervised training on small {`}near target category{'} datasets. In the SemEval Task, our system ranks 14 out of 103 participants.
USF at SemEval-2019 Task 6: Offensive Language Detection Using LSTM With Word Embeddings
Authors: Bharti Goel, Ravi Sharma
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: General Classification, Lemmatization, Word Embeddings

In this paper, we present a system description for the SemEval-2019 Task 6 submitted by our team. For the task, our system takes tweet as an input and determine if the tweet is offensive or non-offensive (Sub-task A). In case a tweet is offensive, our system identifies if a tweet is targeted (insult or threat) or non-targeted like swearing (Sub-task B). In targeted tweets, our system identifies the target as an individual or group (Sub-task C). We used data pre-processing techniques like splitting hashtags into words, removing special characters, stop-word removal, stemming, lemmatization, capitalization, and offensive word dictionary. Later, we used keras tokenizer and word embeddings for feature extraction. For classification, we used the LSTM (Long short-term memory) model of keras framework. Our accuracy scores for Sub-task A, B and C are \textit{0.8128}, \textit{0.8167} and \textit{0.3662} respectively. Our results indicate that fine-grained classification to identify offense target was difficult for the system. Lastly, in the future scope section, we will discuss the ways to improve system performance.
UTFPR at SemEval-2019 Task 6: Relying on Compositionality to Find Offense
Authors: Gustavo Henrique Paetzold
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Sentence

We 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 that, although not very competitive for the task at hand, it offers a robust solution to the orthographic irregularity inherent to tweets.
UVA Wahoos at SemEval-2019 Task 6: Hate Speech Identification using Ensemble Machine Learning
Authors: Murugesan Ramakrishnan, Wlodek Zadrozny, Narges Tabari
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: BIG-bench Machine Learning

With the growth in the usage of social media, it has become increasingly common for people to hide behind a mask and abuse others. We have attempted to detect such tweets and comments that are malicious in intent, which either targets an individual or a group. Our best classifier for identifying offensive tweets for SubTask A (Classifying offensive vs. nonoffensive) has an accuracy of 83.14{\%} and a f1- score of 0.7565 on the actual test data. For SubTask B, to identify if an offensive tweet is targeted (If targeted towards an individual or a group), the classifier performs with an accuracy of 89.17{\%} and f1-score of 0.5885. The paper talks about how we generated linguistic and semantic features to build an ensemble machine learning model. By training with more extracts from different sources (Facebook, and more tweets), the paper shows how the accuracy changes with additional training data.
YNU-HPCC at SemEval-2019 Task 6: Identifying and Categorising Offensive Language on Twitter
Authors: Chengjin Zhou, Jin Wang, Xue-jie Zhang
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Deep Learning

This document describes the submission of team YNU-HPCC to SemEval-2019 for three Sub-tasks of Task 6: Sub-task A, Sub-task B, and Sub-task C. We have submitted four systems to identify and categorise offensive language. The first subsystem is an attention-based 2-layer bidirectional long short-term memory (BiLSTM). The second subsystem is a voting ensemble of four different deep learning architectures. The third subsystem is a stacking ensemble of four different deep learning architectures. Finally, the fourth subsystem is a bidirectional encoder representations from transformers (BERT) model. Among our models, in Sub-task A, our first subsystem performed the best, ranking 16th among 103 teams; in Sub-task B, the second subsystem performed the best, ranking 12th among 75 teams; in Sub-task C, the fourth subsystem performed best, ranking 4th among 65 teams.
YNUWB at SemEval-2019 Task 6: K-max pooling CNN with average meta-embedding for identifying offensive language
Authors: Bin Wang, Xiaobing Zhou, Xue-jie Zhang
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: valid

This paper describes the system submitted to SemEval 2019 Task 6: OffensEval 2019. The task aims to identify and categorize offensive language in social media, we only participate in Sub-task A, which aims to identify offensive language. In order to address this task, we propose a system based on a K-max pooling convolutional neural network model, and use an argument for averaging as a valid meta-embedding technique to get a metaembedding. Finally, we also use a cyclic learning rate policy to improve model performance. Our model achieves a Macro F1-score of 0.802 (ranked 9/103) in the Sub-task A.