@inproceedings{seganti-etal-2019-nlpr,
    title = "{NLPR}@{SRPOL} at {S}em{E}val-2019 Task 6 and Task 5: Linguistically enhanced deep learning offensive sentence classifier",
    author = "Seganti, Alessandro  and
      Sobol, Helena  and
      Orlova, Iryna  and
      Kim, Hannam  and
      Staniszewski, Jakub  and
      Krumholc, Tymoteusz  and
      Koziel, Krystian",
    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://aclanthology.org/S19-2126/",
    doi = "10.18653/v1/S19-2126",
    pages = "712--721",
    abstract = "The paper presents a system developed for the SemEval-2019 competition Task 5 hat- Eval Basile et al. (2019) (team name: LU Team) and Task 6 OffensEval Zampieri et al. (2019b) (team name: NLPR@SRPOL), where we achieved 2nd position in Subtask C. The system combines in an ensemble several models (LSTM, Transformer, OpenAI{'}s GPT, Random forest, SVM) with various embeddings (custom, ELMo, fastText, Universal Encoder) together with additional linguistic features (number of blacklisted words, special characters, etc.). The system works with a multi-tier blacklist and a large corpus of crawled data, annotated for general offensiveness. In the paper we do an extensive analysis of our results and show how the combination of features and embedding affect the performance of the models."
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        <title>NLPR@SRPOL at SemEval-2019 Task 6 and Task 5: Linguistically enhanced deep learning offensive sentence classifier</title>
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        <namePart type="given">Alessandro</namePart>
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            <title>Proceedings of the 13th International Workshop on Semantic Evaluation</title>
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            <namePart type="given">Ekaterina</namePart>
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    <abstract>The paper presents a system developed for the SemEval-2019 competition Task 5 hat- Eval Basile et al. (2019) (team name: LU Team) and Task 6 OffensEval Zampieri et al. (2019b) (team name: NLPR@SRPOL), where we achieved 2nd position in Subtask C. The system combines in an ensemble several models (LSTM, Transformer, OpenAI’s GPT, Random forest, SVM) with various embeddings (custom, ELMo, fastText, Universal Encoder) together with additional linguistic features (number of blacklisted words, special characters, etc.). The system works with a multi-tier blacklist and a large corpus of crawled data, annotated for general offensiveness. In the paper we do an extensive analysis of our results and show how the combination of features and embedding affect the performance of the models.</abstract>
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%0 Conference Proceedings
%T NLPR@SRPOL at SemEval-2019 Task 6 and Task 5: Linguistically enhanced deep learning offensive sentence classifier
%A Seganti, Alessandro
%A Sobol, Helena
%A Orlova, Iryna
%A Kim, Hannam
%A Staniszewski, Jakub
%A Krumholc, Tymoteusz
%A Koziel, Krystian
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F seganti-etal-2019-nlpr
%X The paper presents a system developed for the SemEval-2019 competition Task 5 hat- Eval Basile et al. (2019) (team name: LU Team) and Task 6 OffensEval Zampieri et al. (2019b) (team name: NLPR@SRPOL), where we achieved 2nd position in Subtask C. The system combines in an ensemble several models (LSTM, Transformer, OpenAI’s GPT, Random forest, SVM) with various embeddings (custom, ELMo, fastText, Universal Encoder) together with additional linguistic features (number of blacklisted words, special characters, etc.). The system works with a multi-tier blacklist and a large corpus of crawled data, annotated for general offensiveness. In the paper we do an extensive analysis of our results and show how the combination of features and embedding affect the performance of the models.
%R 10.18653/v1/S19-2126
%U https://aclanthology.org/S19-2126/
%U https://doi.org/10.18653/v1/S19-2126
%P 712-721
Markdown (Informal)
[NLPR@SRPOL at SemEval-2019 Task 6 and Task 5: Linguistically enhanced deep learning offensive sentence classifier](https://aclanthology.org/S19-2126/) (Seganti et al., SemEval 2019)
ACL
- Alessandro Seganti, Helena Sobol, Iryna Orlova, Hannam Kim, Jakub Staniszewski, Tymoteusz Krumholc, and Krystian Koziel. 2019. NLPR@SRPOL at SemEval-2019 Task 6 and Task 5: Linguistically enhanced deep learning offensive sentence classifier. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 712–721, Minneapolis, Minnesota, USA. Association for Computational Linguistics.