A Unified System for Aggression Identification in English Code-Mixed and Uni-Lingual Texts
Table 4. Facebook Test results(TRAC 2018 Dataset)
['[BOLD] Rank in Trac 2018', '[BOLD] System', '[BOLD] F1 (Weighted) Facebook']
To compare our results on Kaggle dataset, we have used the last & the best published result on Kaggle website as a baseline. We have conducted the separate experiments, to properly investigate the performance of (a) each of the classifiers (used in our model averaging based system), (b) impact of the NLP features on each of these classifiers and finally, (c) the performance of our proposed system. Similarly, DPCNN+NLP Features, DRNN + NLP Features and Pooled BiLSTM + NLP Features are corresponding models with NLP features. The Model Averaging (A+B+C) is the ensemble of three models (i.e., model averaging of DPCNN, DRNN and Pooled BiLSTM) without NLP features. Finally, Our Proposed Method, which represents the model averaging of three models with NLP features. In this paper, we have evaluated our model using weighted macro-averaged F-score. It weights the F-score computed per class based on the class composition in the test set and then takes the average of these per-class F-score gives the final F-score. presents the comparative experimental results for the proposed method in this paper with respect to the state-of-the-art. The top 5 models(Kumar are the best performing models for Facebook and Twitter test dataset respectively on TRAC 2018. it is clear that our proposed model shows the best performance among all of the approaches. These results also state that all the deep learning architectures with NLP features, perform better than individual corresponding deep learning architectures. This means NLP features, adds some value to the architectures, even if it is not very high.