Tw-StAR at SemEval-2019 Task 5: N-gram embeddings for Hate Speech Detection in Multilingual Tweets
Hala Mulki, Chedi Bechikh Ali, Hatem Haddad, İsmail Babaoğlu · 2019
In this paper, we describe our contribution in SemEval-2019: subtask A of task 5 "Multilingual detection of hate speech against immigrants and women in Twitter (HatEval)".We developed two hate speech detection model variants through Tw-StAR framework.While the first model adopted one-hot encoding ngrams to train an NB classifier, the second generated and learned n-gram embeddings within a feedforward neural network.For both models, specific terms, selected via MWT patterns, were tagged in the input data.With two feature types employed, we could investigate the ability of n-gram embeddings to rival one-hot n-grams.Our results showed that in English, n-gram embeddings outperformed one-hot ngrams.However, representing Spanish tweets by one-hot n-grams yielded a slightly better performance compared to that of n-gram embeddings.The official ranking indicated that Tw-StAR ranked 9 th for English and 20 th for Spanish.