[PDF] Top 20 UTFPR at SemEval 2019 Task 5: Hate Speech Identification with Recurrent Neural Networks

... identifying hate speech in posts from social media. We approach the task us- ing a system based on minimalistic compo- sitional Recurrent Neural Networks ...the ... See full document

... the task, a CNN was im- plemented based on the architecture proposed by (Zhang and Wallace, 2015), and this implementa- tion can be divided in two steps: feature extrac- tion and ... See full document

... The main goal of this paper is to show that sim- pler models which have understandable features can produce good results. More complex methods like introducing polynomial or intricate features, deep learning models using ... See full document

... of hate speech de- ...fication task, we saw that there were many es- tablished approaches to solving this problem - various machine learning techniques, according to our research, were shown to be ... See full document

... from Recurrent Neural Networks to recently proposed language models (Sabour et ...customized hate lexicons derived by the detection of language patterns in the train- ing set has been ... See full document

... shared task at IberEval 2018 the best re- sults were obtained with Support Vector Machine models with different feature configurations, there are also a few notable neural networks techniques ... See full document

... Hate speech detection problem has been ad- dressed as a machine learning classification ...volutional Neural Network (CNN), Long Short- Term Memory (LSTM) and Recurrent Neural Net- work ... See full document

... on hate speech and offensive lan- guage detection has mostly focused on supervised machine learning techniques (Mathur et ...shared task on identifying aggression in social media (Kumar et ...of ... See full document

... a recurrent neural network classifier that was pre-trained via an auxiliary masked hashtag prediction ...the hate speech positive and negative ... See full document

... with neural network archi- tectures: multilayer perceptrons (MLP), convo- lutional neural networks (CNN), bi-directional LSTMs and a combination of LSTMs and CNNs (LSTM + ... See full document

... Previous work has studied several types of on- line misbehavior such as aggression (Cheng et al., 2015), cyberbullying (Pieschl et al., 2015), hate speech (Saleem et al., 2017), offensive, and abu- sive ... See full document

... for hate speech ...for hate speech ...of recurrent neural network (RNN) classifiers and incorporated var- ious features associated with user-related infor- ...convolutional ... See full document

... free speech, other issues could emerge such as the usage of offensive language that could mock or insult individuals or groups of ...language, hate speech, cyberbullying, trolling, among others ... See full document

... • UTFPR-Reuse: The same model depicted in Figure 1, except instead of training its character-to-word RNN layers from scratch along with the rest of the model, they are taken from a similar compositional model ... See full document

... automatic hate speech detection in social media, the problem is far from solved, partly due to the low consensus on what exactly constitutes hate speech, how it relates to offensive language ... See full document

... is hate speech de- tection, as manual detection has been made nearly impossible by the quantity of ...automated hate speech detection. Our task is detection of hate speech ... See full document

... Another approach is to employ word embed- dings in order to capture similarities between words (Badjatiya et al., 2017). Extending this method, Djuric et al. (2015) used paragraph2vec (Le and Mikolov, 2014) to encode ... See full document

... (2018) observed that extremist violence tends to lead to an increase in online hate speech, partic- ularly on messages directly advocating violence. Also, Anzovino et al. (2018) contributed to the re- ... See full document

... the Task 5 of Semeval-2019, which aims at detecting hate speech against immigrants and women in ...The task consists of two sub- tasks, in Spanish and English: (A) ... See full document

... the SemEval 2019 shared task 1 ‘Cross-lingual Semantic Parsing with ...shared task which are converted from the original UCCA files and model the task as tag- ...proposed neural ... See full document