Emmanuel Osei-Brefo


2024

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NU at WASSA 2024 Empathy and Personality Shared Task: Enhancing Personality Predictions with Knowledge Graphs; A Graphical Neural Network and LightGBM Ensemble Approach
Emmanuel Osei-Brefo | Huizhi Liang
Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis

This paper proposes a novel ensemble approach that combines Graph Neural Networks (GNNs) and LightGBM to enhance personality prediction based on the personality Big 5 model. By integrating BERT embeddings from user essays with knowledge graph-derived embeddings, our method accurately captures rich semantic and relational information. Additionally, a special loss function that combines Mean Squared Error (MSE), Pearson correlation loss, and contrastive loss to improve model performance is introduced. The proposed ensemble model, made of Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and LightGBM, demonstrates superior performance over other models, with significant improvements in prediction accuracy for the Big Five personality traits achieved. Our system officially ranked 2nd at the Track 4: PER track.

2022

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UoR-NCL at SemEval-2022 Task 6: Using ensemble loss with BERT for intended sarcasm detection
Emmanuel Osei-Brefo | Huizhi Liang
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

Sarcasm has gained notoriety for being difficult to detect by machine learning systems due to its figurative nature. In this paper, Bidirectional Encoder Representations from Transformers (BERT) model has been used with ensemble loss made of cross-entropy loss and negative log-likelihood loss to classify whether a given sentence is in English and Arabic tweets are sarcastic or not. From the results obtained in the experiments, our proposed BERT with ensemble loss achieved superior performance when applied to English and Arabic test datasets. For the validation dataset, our model performed better on the Arabic dataset but failed to outperform the baseline method (made of BERT with only a single loss function) when applied on the English validation set.

2021

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UOR at SemEval-2021 Task 12: On Crowd Annotations; Learning with Disagreements to optimise crowd truth
Emmanuel Osei-Brefo | Thanet Markchom | Huizhi Liang
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

Crowdsourcing has been ubiquitously used for annotating enormous collections of data. However, the major obstacles to using crowd-sourced labels are noise and errors from non-expert annotations. In this work, two approaches dealing with the noise and errors in crowd-sourced labels are proposed. The first approach uses Sharpness-Aware Minimization (SAM), an optimization technique robust to noisy labels. The other approach leverages a neural network layer called softmax-Crowdlayer specifically designed to learn from crowd-sourced annotations. According to the results, the proposed approaches can improve the performance of the Wide Residual Network model and Multi-layer Perception model applied on crowd-sourced datasets in the image processing domain. It also has similar and comparable results with the majority voting technique when applied to the sequential data domain whereby the Bidirectional Encoder Representations from Transformers (BERT) is used as the base model in both instances.

2020

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UoR at SemEval-2020 Task 8: Gaussian Mixture Modelling (GMM) Based Sampling Approach for Multi-modal Memotion Analysis
Zehao Liu | Emmanuel Osei-Brefo | Siyuan Chen | Huizhi Liang
Proceedings of the Fourteenth Workshop on Semantic Evaluation

Memes are widely used on social media. They usually contain multi-modal information such as images and texts, serving as valuable data sources to analyse opinions and sentiment orientations of online communities. The provided memes data often face an imbalanced data problem, that is, some classes or labelled sentiment categories significantly outnumber other classes. This often results in difficulty in applying machine learning techniques where balanced labelled input data are required. In this paper, a Gaussian Mixture Model sampling method is proposed to tackle the problem of class imbalance for the memes sentiment classification task. To utilise both text and image data, a multi-modal CNN-LSTM model is proposed to jointly learn latent features for positive, negative and neutral category predictions. The experiments show that the re-sampling model can slightly improve the accuracy on the trial data of sub-task A of Task 8. The multi-modal CNN-LSTM model can achieve macro F1 score 0.329 on the test set.