Malak Abdullah


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JUSTMasters at SemEval-2020 Task 3: Multilingual Deep Learning Model to Predict the Effect of Context in Word Similarity
Nour Al-khdour | Mutaz Bni Younes | Malak Abdullah | Mohammad AL-Smadi
Proceedings of the Fourteenth Workshop on Semantic Evaluation

There is a growing research interest in studying word similarity. Without a doubt, two similar words in a context may considered different in another context. Therefore, this paper investigates the effect of the context in word similarity. The SemEval-2020 workshop has provided a shared task (Task 3: Predicting the (Graded) Effect of Context in Word Similarity). In this task, the organizers provided unlabeled datasets for four languages, English, Croatian, Finnish and Slovenian. Our team, JUSTMasters, has participated in this competition in the two subtasks: A and B. Our approach has used a weighted average ensembling method for different pretrained embeddings techniques for each of the four languages. Our proposed model outperformed the baseline models in both subtasks and acheived the best result for subtask 2 in English and Finnish, with score 0.725 and 0.68 respectively. We have been ranked the sixth for subtask 1, with scores for English, Croatian, Finnish, and Slovenian as follows: 0.738, 0.44, 0.546, 0.512.

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TeamJUST at SemEval-2020 Task 4: Commonsense Validation and Explanation Using Ensembling Techniques
Roweida Mohammed | Malak Abdullah
Proceedings of the Fourteenth Workshop on Semantic Evaluation

Common sense for natural language processing methods has been attracting a wide research interest, recently. Estimating automatically whether a sentence makes sense or not is considered an essential question. Task 4 in the International Workshop SemEval 2020 has provided three subtasks (A, B, and C) that challenges the participants to build systems for distinguishing the common sense statements from those that do not make sense. This paper describes TeamJUST’s approach for participating in subtask A to differentiate between two sentences in English and classify them into two classes: common sense and uncommon sense statements. Our approach depends on ensembling four different state-of-the-art pre-trained models (BERT, ALBERT, Roberta, and XLNet). Our baseline model which we used only the pre-trained model of BERT has scored 89.1, while the TeamJUST model outperformed the baseline model with an accuracy score of 96.2. We have improved the results in the post-evaluation period to achieve our best result, which would rank the 4th in the competition if we had the chance to use our latest experiment.

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MLEngineer at SemEval-2020 Task 7: BERT-Flair Based Humor Detection Model (BFHumor)
Fara Shatnawi | Malak Abdullah | Mahmoud Hammad
Proceedings of the Fourteenth Workshop on Semantic Evaluation

Task 7, Assessing the Funniness of Edited News Headlines, in the International Workshop SemEval2020 introduces two sub-tasks to predict the funniness values of edited news headlines from the Reddit website. This paper proposes the BFHumor model of the MLEngineer team that participates in both sub-tasks in this competition. The BFHumor’s model is defined as a BERT-Flair based humor detection model that is a combination of different pre-trained models with various Natural Language Processing (NLP) techniques. The Bidirectional Encoder Representations from Transformers (BERT) regressor is considered the primary pre-trained model in our approach, whereas Flair is the main NLP library. It is worth mentioning that the BFHumor model has been ranked 4th in sub-task1 with a root mean square error (RMSE) value of 0.51966, and it is 0.02 away from the first ranked model. Also, the team is ranked 12th in the sub-task2 with an accuracy of 0.62291, which is 0.05 away from the top-ranked model. Our results indicate that the BFHumor model is one of the top models for detecting humor in the text.

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JUST at SemEval-2020 Task 11: Detecting Propaganda Techniques Using BERT Pre-trained Model
Ola Altiti | Malak Abdullah | Rasha Obiedat
Proceedings of the Fourteenth Workshop on Semantic Evaluation

This paper presents the submission to semeval-2020 task 11, Detection of Propaganda Techniques in News Articles. Knowing that there are two subtasks in this competition, we have participated in the Technique Classification subtask (TC), which aims to identify the propaganda techniques used in a specific propaganda span. We have used and implemented various models to detect propaganda. Our proposed model is based on BERT uncased pre-trained language model as it has achieved state-of-the-art performance on multiple NLP benchmarks. The performance results of our proposed model have scored 0.55307 F1-Score, which outperforms the baseline model provided by the organizers with 0.2519 F1-Score, and our model is 0.07 away from the best performing team. Compared to other participating systems, our submission is ranked 15th out of 31 participants.


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JUSTDeep at NLP4IF 2019 Task 1: Propaganda Detection using Ensemble Deep Learning Models
Hani Al-Omari | Malak Abdullah | Ola AlTiti | Samira Shaikh
Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda

The internet and the high use of social media have enabled the modern-day journalism to publish, share and spread news that is difficult to distinguish if it is true or fake. Defining “fake news” is not well established yet, however, it can be categorized under several labels: false, biased, or framed to mislead the readers that are characterized as propaganda. Digital content production technologies with logical fallacies and emotional language can be used as propaganda techniques to gain more readers or mislead the audience. Recently, several researchers have proposed deep learning (DL) models to address this issue. This research paper provides an ensemble deep learning model using BiLSTM, XGBoost, and BERT to detect propaganda. The proposed model has been applied on the dataset provided by the challenge NLP4IF 2019, Task 1 Sentence Level Classification (SLC) and it shows a significant performance over the baseline model.

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EmoDet at SemEval-2019 Task 3: Emotion Detection in Text using Deep Learning
Hani Al-Omari | Malak Abdullah | Nabeel Bassam
Proceedings of the 13th International Workshop on Semantic Evaluation

Task 3, EmoContext, in the International Workshop SemEval 2019 provides training and testing datasets for the participant teams to detect emotion classes (Happy, Sad, Angry, or Others). This paper proposes a participating system (EmoDet) to detect emotions using deep learning architecture. The main input to the system is a combination of Word2Vec word embeddings and a set of semantic features (e.g. from AffectiveTweets Weka-package). The proposed system (EmoDet) ensembles a fully connected neural network architecture and LSTM neural network to obtain performance results that show substantial improvements (F1-Score 0.67) over the baseline model provided by Task 3 organizers (F1-score 0.58).


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TeamUNCC at SemEval-2018 Task 1: Emotion Detection in English and Arabic Tweets using Deep Learning
Malak Abdullah | Samira Shaikh
Proceedings of The 12th International Workshop on Semantic Evaluation

Task 1 in the International Workshop SemEval 2018, Affect in Tweets, introduces five subtasks (El-reg, El-oc, V-reg, V-oc, and E-c) to detect the intensity of emotions in English, Arabic, and Spanish tweets. This paper describes TeamUNCC’s system to detect emotions in English and Arabic tweets. Our approach is novel in that we present the same architecture for all the five subtasks in both English and Arabic. The main input to the system is a combination of word2vec and doc2vec embeddings and a set of psycholinguistic features (e.g. from AffectTweets Weka-package). We apply a fully connected neural network architecture and obtain performance results that show substantial improvements in Spearman correlation scores over the baseline models provided by Task 1 organizers, (ranging from 0.03 to 0.23). TeamUNCC’s system ranks third in subtask El-oc and fourth in other subtasks for Arabic tweets.