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IR3218-UI at SemEval-2020 Task 12: Emoji Effects on Offensive Language IdentifiCation

2020-12-01 · SEMEVAL 2020 · Sandy Kurniawan, Indra Budi, Muhammad Okky Ibrohim

In this paper, we present our approach and the results of our participation in OffensEval 2020. There are three sub-tasks in OffensEval 2020 namely offensive language identification (sub-task A), automatic categorization of offense types (sub-task B), and offense target identification (sub-task C). We participated in sub-task A of English OffensEval 2020. Our approach emphasizes on how the emoji affects offensive language identification. Our model used LSTM combined with GloVe pre-trained word vectors to identify offensive language on social media. The best model obtained macro F1-score of 0.88428.

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Language Identification

Methods 이 논문이 사용한 방법론

Tanh Activation 설명 없음
GloVe GloVe Embeddings are a type of word embedding that encode the co-occurrence probability ratio between two words as vector differences. GloVe uses a weighted least squares…
Sigmoid Activation 설명 없음
LSTM An LSTM is a type of recurrent neural network that addresses the vanishing gradient problem in vanilla…

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