[PDF] Top 20 SemEval 2019 Task 4: Hyperpartisan News Detection

... Of the top teams, only Sally Smedley used the by-publisher dataset, and only to select n-grams. Based on the reports of several teams, the utiliza- tion of this dataset thus seems more difficult than we expected. We ... See full document

... This paper summarized our participation in SemEval-2019 Task 4, where we aimed at the challenge of Hyperpartisan News Detection. We tried to approach the problem from the ... See full document

... In this paper, we introduce the ELMo Sentence Representation Convolutional (ESRC) Network. We first pre-calculate sentence level embeddings as the average of ELMo (Peters et al., 2018) word embeddings for each sentence, ... See full document

... A consistent result for all the tested classifiers was that they performed better when creating the n-gram features described in last section from the text context of the news articles rather than only using the ... See full document

... In this work, we have explored traditional sets of features and models for the Hyper-partisan News Detection problem. We worked on two corpora, of which one has been used in the state-of-the-art literature. ... See full document

... to 4), and this output was passed to a bidirectional LSTM layer which produces two 100d vec- tor outputs, which after concatenation, were passed to a final 2d softmax ... See full document

... In this paper, we described a stacked BiLSTM deep learning model to detect emotion in context. We used glove pre-trained embeddings to convert each word into its corresponding word vector and then passed it on to two ... See full document

... The datasets are collections of labeled conversa- tions (Chatterjee et al., 2019b). Each conversation is a three turn talk between two persons. The con- versation labels correspond to the emotional state of the last ... See full document

... bc-LSTM (Poria et al., 2017) consists of five layers: 1) embedding layer; 2) input layer; 3) LSTM layer; 4) dense layer; and 5) softmax layer. The embedding layer (shared across three utter- ances) converts ... See full document

... Hyper-parameter selection for CNN was diffi- cult, and we restricted to LSTM for the Phase 2 (i.e. test phase). We also noticed that the LSTM model was overfitting early in the train- ing process (4-5 epochs) and ... See full document

... the SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter (HatEval) shared ...classification task in which systems are trained to ... See full document

... The final submitted result to the challenge is shown in Table 3. The metric used for the chal- lenge is the microaveraged F1 score (F1μ) for the three emotion classes, i.e. Happy, Sad and An- gry. Our model performance ... See full document

... Naive Bayes family. The Glove-Twitter version of logreg and XGboost aren’t too far behind as well. We applied all these high performing models on the dev set to analyse their performance further. The results are shown in ... See full document

... HS detection tools in several application ...this task (see Section 4) has improved the possibility of in-depth investigation of the involved ... See full document

... As can be seen from Table 4, the BiLSTM model without attention outperformed the other two models for all the metrics. However, the improvements achieved were not very significant compared to the other two models. ... See full document

... mEval 2019 Task 5: Multilingual detection of hate speech against immigrants and women in ...both task A (Hate Speech Detection against Immigrants and Women) and task B (Aggres- ... See full document

... Our data comes from two corpora. The first one, is an already existing corpus containing English tweets annotated for hate speech against immi- grants and women, as part of the HatEval task at SemEval2019 (Basile ... See full document

... The detection of hate speech, especially in on- line platforms and forums, is quickly becom- ing a hot topic as anti-hate speech legislation begins to be applied to public discourse on- ...shared task was ... See full document

... For our RF model we executed a grid search start- ing with the following parameters: character n- grams with range: 2-3, 2-4, 1-3, 1-4; word n- grams with range 1, 1-2, 1-3, 1-4 and all combi- ... See full document

... Google News word2vec 2 embed- dings and 300D pre-trained ...vectors 4 which were pre-trained on a big col- lection of Twitter messages using GloVe are also ... See full document