Fermi at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media using Sentence Embeddings

Indurthi, Vijayasaradhi; Syed, Bakhtiyar; Shrivastava, Manish; Gupta, Manish; Varma, Vasudeva

doi:10.18653/v1/s19-2109

Cited by 8 publications

(3 citation statements)

References 22 publications

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“…Pelicon et al achieved a 0.8078 F-score with BERT [30]. In [31] with machine learning algorithms that were fed into multiple sentence embeddings 64.40% F-score was obtained. Results showed that the deep model dominated the SVM models with a 0.7793 F-score.…”

Section: Related Workmentioning

confidence: 99%

Classification of Imbalanced Offensive Dataset – Sentence Generation for Minority Class with LSTM

Ekinci¹

2022

Sakarya University Journal of Computer and Information Sciences

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The classification of documents is one of the problems studied since ancient times and still continues to be studied. With social media becoming a part of daily life and its misuse, the importance of text classification has started to increase. This paper investigates the effect of data augmentation with sentence generation on classification performance in an imbalanced dataset. We propose an LSTM based sentence generation method, Term Frequency-Inverse Document Frequency (TF-IDF) and Word2vec and apply Logistic Regression (LR), Support Vector Machine (SVM), K Nearest Neighbor (KNN), Multilayer Perceptron (MLP), Extremely Randomized Trees (Extra tree), Random Forest, eXtreme Gradient Boosting (Xgboost), Adaptive Boosting (AdaBoost) and Bagging. Our experiment results on an imbalanced Offensive Language Identification Dataset (OLID) that machine learning with sentence generation significantly outperforms.

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Section: Related Workmentioning

confidence: 99%

Classification of Imbalanced Offensive Dataset – Sentence Generation for Minority Class with LSTM

Ekinci¹

2022

Sakarya University Journal of Computer and Information Sciences

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“…Meme text: Global Vectors for Word Representation (GloVe) Word embeddings represent the semantic and syntactic meaning of the words as dense vector representations. It has improved the performance of several downstream tasks across various domains like text classification, machine comprehension etc., (Indurthi et al, 2019). GloVe (Pennington et al, 2014) embeddings based on twitter corpus was used for encoding the words of each meme text.…”

Section: Meme Images: Inception Networkmentioning

confidence: 99%

Memebusters at SemEval-2020 Task 8: Feature Fusion Model for Sentiment Analysis on Memes Using Transfer Learning

Sharma¹,

Kandasamy²,

Vasantha³

2020

Proceedings of the Fourteenth Workshop on Semantic Evaluation

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In this paper, we describe our deep learning system used for SemEval 2020 Task 8: Memotion analysis. We participated in all the subtasks i.e Subtask A: Sentiment classification, Subtask B: Humor classification, and Subtask C: Scales of semantic classes. Similar multimodal architecture was used for each subtask. The proposed architecture makes use of transfer learning for images and text feature extraction. The extracted features are then fused together using stacked bidirectional Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) model with attention mechanism for final predictions. We also propose a single model for predicting semantic classes (Subtask B) as well as their scales (Subtask C) by branching the final output of the post LSTM dense layers. Our model was ranked 5 in Subtask B and ranked 8 in Subtask C and performed nicely in Subtask A on the leader board. Our system makes use of transfer learning for feature extraction and fusion of image and text features for predictions.

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“…Offensive language identification has seen extensive usage of language modeling approaches like BERT (Pelicon et al, 2019;Pavlopoulos et al, 2019;Wu et al, 2019;Liu et al, 2019), GPT (Zampieri et al, 2019b) and ELMo (Indurthi et al, 2019) with varying hyperparameters and pre-processing steps. In this work, based on its widespread usage, BERT (Devlin et al, 2019) is used as the classifier.…”

Section: Classifiermentioning

confidence: 99%

Hitachi at SemEval-2020 Task 12: Offensive Language Identification with Noisy Labels Using Statistical Sampling and Post-Processing

Ravikiran

Muljibhai

Miyoshi

et al. 2020

Proceedings of the Fourteenth Workshop on Semantic Evaluation

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In this paper, we present our participation in SemEval-2020 Task-12 Subtask-A (English Language) which focuses on offensive language identification from noisy labels. To this end, we developed a hybrid system with the BERT classifier trained with tweets selected using Statistical Sampling Algorithm (SA) and Post-Processed (PP) using an offensive wordlist. Our developed system achieved 34 th position with Macro-averaged F1-score (Macro-F1) of 0.90913 over both offensive and non-offensive classes. We further show comprehensive results and error analysis to assist future research in offensive language identification with noisy labels.

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Fermi at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media using Sentence Embeddings

Cited by 8 publications

References 22 publications

Classification of Imbalanced Offensive Dataset – Sentence Generation for Minority Class with LSTM

Classification of Imbalanced Offensive Dataset – Sentence Generation for Minority Class with LSTM

Memebusters at SemEval-2020 Task 8: Feature Fusion Model for Sentiment Analysis on Memes Using Transfer Learning

Hitachi at SemEval-2020 Task 12: Offensive Language Identification with Noisy Labels Using Statistical Sampling and Post-Processing

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