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TECHSSN at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Tweets using Deep Neural Networks

2019-06-01 · SEMEVAL 2019 6 · Angel Suseelan, Rajalakshmi S, Logesh B, Harshini S, Geetika B, Dyaneswaran S, S Milton Rajendram, Mirnalinee T T

Task 6 of SemEval 2019 involves identifying and categorizing offensive language in social media. The systems developed by TECHSSN team uses multi-level classification techniques. We have developed two systems. In the first system, the first level of classification is done by a multi-branch 2D CNN classifier with Google{'}s pre-trained Word2Vec embedding and the second level of classification by string matching technique supported by offensive and bad words dictionary. The second system uses a multi-branch 1D CNN classifier with Glove pre-trained embedding layer for the first level of classification and string matching for the second level of classification. Input data with a probability of less than 0.70 in the first level are passed on to the second level. The misclassified examples are classified correctly in the second level.

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ClassificationGeneral Classification

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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…

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