[PDF] Top 20 Atalaya at SemEval 2019 Task 5: Robust Embeddings for Tweet Classification

... subword-aware embeddings library (Bojanowski et ...own embeddings on a dataset of ∼ 90 million tweets from various Spanish-speaking ...skip-gram embeddings were trained using different parameter ... 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 deployed in order to ... See full document

... • Hate indicatives tagging: Multi-word terms (MWT) are meaning indicators of a sen- tence/document (Henry et al., 2018; Bechikh- Ali et al., 2019). In our case, they can rep- resent the entities discussed within a ... See full document

... We used a second SVM classifier within our en- semble model, but this time with word embed- dings as its input. This choice is motivated by the hypothesis that introducing different predic- tions given by models trained ... See full document

... a tweet in English or in Spanish with a given target (women or immigrants) is hateful or not ...hateful. TASK B (Aggressive behavior and Target Clas- sification) is a two-class classification where ... See full document

... word embeddings using distant training (Cliche, 2017) and tweet sentence representations Deep- Moji (Felbo et ...this task we explored differ- ent word and sentence ...word embeddings and ... See full document

... ter embeddings are passed onto a set of bidirec- tional RNN layers that produces word representa- tions, then a second set of layers produces a final representation of the ... See full document

... 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 of- fensive or non-offensive (Sub-task ...a ... See full document

... in Task A, as tweets could only be labeled as positive for tar- geting or aggression if they were positive for hate ...for Task B was Exact Match Ratio (EMR), which is the propor- tion of tweets that are ... 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 ...a tweet, but also the ... See full document

... In this paper, we present our approach to de- tection of hate speech against women and im- migrants in tweets for our participation in the SemEval-2019 Task 5. We trained an SVM and an RF ... See full document

... Emotional Embeddings These refer to two types of features equipped with emotional knowl- edge. The first is a word-level emotional repre- sentation called Emo2Vec (Xu et al., 2018). It is trained with six ... See full document

... We present three system configurations for both tasks. B4MSA uses only the training data pro- vided by the contest as the knowledge base to classify texts, i.e., B4MSA is our baseline, but it is also its outcome is an ... See full document

... We saved the best epoch model for each of the 10 splits and we used them to make our final pre- diction for the test dataset. Then we used our 10 models to classify each tweet: to predict a tweet as ... See full document

... this task, the data has been delivered in the same way than sub-task A, so we emulated the same workflow than before, but in this case, con- sidering solely hateful ...al., 2019) along languages and ... See full document

... We can also see that only in this category the CNN based models outperformed LSTM based models. This implies that for adversarial learn- ing to work, one has to use a very robust feature extractor. It is also the ... See full document

... 2011), classification involves detecting opinions about a specific target rather than detecting the more gen- eral opinion expressed in a given ...the tweet as a whole, in the second set the value was to be ... See full document

... eliminate task specific depen- dencies as result we did not included any task specificity into the ...use task specific transfer learning ...one task however reducing our accuracy ... See full document

... 2019; Webb et al., 2005). In addition, in the re- sults shown in Table 1, we can observe that the di- alogue level attention LSTM achieves a high recall but low precision. In contrast, the differences in all ... See full document

... Table 1 below shows the proportion of positive and negative instances of hate speech in the train, de- velopment and test data sets. As can be seen, 42% of the instances in each of the data set are hate speech. The data ... See full document