NLPR@SRPOL at SemEval-2019 Task 6 and Task 5: Linguistically enhanced deep learning offensive sentence classifier
| Autor: | Alessandro Seganti, Hannam Kim, Jakub Staniszewski, Helena Sobol, Iryna Orlova, Tymoteusz Krumholc, Krystian Koziel |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
050101 languages & linguistics Computer Science - Computation and Language Computer science business.industry Deep learning 05 social sciences 010501 environmental sciences computer.software_genre 01 natural sciences Blacklist SemEval Random forest Support vector machine Embedding 0501 psychology and cognitive sciences Artificial intelligence business computer Computation and Language (cs.CL) Sentence Natural language processing 0105 earth and related environmental sciences Transformer (machine learning model) |
| Zdroj: | SemEval@NAACL-HLT |
| DOI: | 10.48550/arxiv.1904.05152 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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