Nour Al-khdour


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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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Team Alexa at NADI Shared Task
Mutaz Younes | Nour Al-khdour | Mohammad AL-Smadi
Proceedings of the Fifth Arabic Natural Language Processing Workshop

In this paper, we discuss our team’s work on the NADI Shared Task. The task requires classifying Arabic tweets among 21 dialects. We tested out different approaches, and the best one was the simplest one. Our best submission was using Multinational Naive Bayes (MNB) classifier (Small and Hsiao, 1985) with n-grams as features. Despite its simplicity, this classifier shows better results than complicated models such as BERT. Our best submitted score was 17% F1-score and 35% accuracy.