@inproceedings{suseelan-etal-2019-techssn,
    title = "{TECHSSN} at {S}em{E}val-2019 Task 6: Identifying and Categorizing Offensive Language in Tweets using Deep Neural Networks",
    author = "Balasubramanian, Logesh  and
      Sathish Kumar, Harshini  and
      Bandlamudi, Geetika  and
      Sivasankaran, Dyaneswaran  and
      Sivanaiah, Rajalakshmi  and
      Suseelan, Angel Deborah  and
      Rajendram, Sakaya Milton  and
      Thanka Nadar Thanagathai, Mirnalinee",
    editor = "May, Jonathan  and
      Shutova, Ekaterina  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/sigarab-more-entries-6621/S19-2132/",
    doi = "10.18653/v1/S19-2132",
    pages = "753--758",
    abstract = "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."
}