Hala Mulki


2019

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L-HSAB: A Levantine Twitter Dataset for Hate Speech and Abusive Language
Hala Mulki | Hatem Haddad | Chedi Bechikh Ali | Halima Alshabani
Proceedings of the Third Workshop on Abusive Language Online

Hate speech and abusive language have become a common phenomenon on Arabic social media. Automatic hate speech and abusive detection systems can facilitate the prohibition of toxic textual contents. The complexity, informality and ambiguity of the Arabic dialects hindered the provision of the needed resources for Arabic abusive/hate speech detection research. In this paper, we introduce the first publicly-available Levantine Hate Speech and Abusive (L-HSAB) Twitter dataset with the objective to be a benchmark dataset for automatic detection of online Levantine toxic contents. We, further, provide a detailed review of the data collection steps and how we design the annotation guidelines such that a reliable dataset annotation is guaranteed. This has been later emphasized through the comprehensive evaluation of the annotations as the annotation agreement metrics of Cohen’s Kappa (k) and Krippendorff’s alpha (α) indicated the consistency of the annotations.

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Syntax-Ignorant N-gram Embeddings for Sentiment Analysis of Arabic Dialects
Hala Mulki | Hatem Haddad | Mourad Gridach | Ismail Babaoğlu
Proceedings of the Fourth Arabic Natural Language Processing Workshop

Arabic sentiment analysis models have employed compositional embedding features to represent the Arabic dialectal content. These embeddings are usually composed via ordered, syntax-aware composition functions and learned within deep neural frameworks. With the free word order and the varying syntax nature across the different Arabic dialects, a sentiment analysis system developed for one dialect might not be efficient for the others. Here we present syntax-ignorant n-gram embeddings to be used in sentiment analysis of several Arabic dialects. The proposed embeddings were composed and learned using an unordered composition function and a shallow neural model. Five datasets of different dialects were used to evaluate the produced embeddings in the sentiment analysis task. The obtained results revealed that, our syntax-ignorant embeddings could outperform word2vec model and doc2vec both variant models in addition to hand-crafted system baselines, while a competent performance was noticed towards baseline systems that adopted more complicated neural architectures.

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Tw-StAR at SemEval-2019 Task 5: N-gram embeddings for Hate Speech Detection in Multilingual Tweets
Hala Mulki | Chedi Bechikh Ali | Hatem Haddad | Ismail Babaoğlu
Proceedings of the 13th International Workshop on Semantic Evaluation

In this paper, we describe our contribution in SemEval-2019: subtask A of task 5 “Multilingual detection of hate speech against immigrants and women in Twitter (HatEval)”. We developed two hate speech detection model variants through Tw-StAR framework. While the first model adopted one-hot encoding ngrams to train an NB classifier, the second generated and learned n-gram embeddings within a feedforward neural network. For both models, specific terms, selected via MWT patterns, were tagged in the input data. With two feature types employed, we could investigate the ability of n-gram embeddings to rival one-hot n-grams. Our results showed that in English, n-gram embeddings outperformed one-hot ngrams. However, representing Spanish tweets by one-hot n-grams yielded a slightly better performance compared to that of n-gram embeddings. The official ranking indicated that Tw-StAR ranked 9th for English and 20th for Spanish.

2018

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Tw-StAR at SemEval-2018 Task 1: Preprocessing Impact on Multi-label Emotion Classification
Hala Mulki | Chedi Bechikh Ali | Hatem Haddad | Ismail Babaoğlu
Proceedings of The 12th International Workshop on Semantic Evaluation

In this paper, we describe our contribution in SemEval-2018 contest. We tackled task 1 “Affect in Tweets”, subtask E-c “Detecting Emotions (multi-label classification)”. A multilabel classification system Tw-StAR was developed to recognize the emotions embedded in Arabic, English and Spanish tweets. To handle the multi-label classification problem via traditional classifiers, we employed the binary relevance transformation strategy while a TF-IDF scheme was used to generate the tweets’ features. We investigated using single and combinations of several preprocessing tasks to further improve the performance. The results showed that specific combinations of preprocessing tasks could significantly improve the evaluation measures. This has been later emphasized by the official results as our system ranked 3rd for both Arabic and Spanish datasets and 14th for the English dataset.

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Impact du Prétraitement Linguistique sur l’Analyse de Sentiment du Dialecte Tunisien ()
Chedi Bechikh Ali | Hala Mulki | Hatem Haddad
Actes de la Conférence TALN. Volume 1 - Articles longs, articles courts de TALN

2017

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Tw-StAR at SemEval-2017 Task 4: Sentiment Classification of Arabic Tweets
Hala Mulki | Hatem Haddad | Mourad Gridach | Ismail Babaoglu
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

In this paper, we present our contribution in SemEval 2017 international workshop. We have tackled task 4 entitled “Sentiment analysis in Twitter”, specifically subtask 4A-Arabic. We propose two Arabic sentiment classification models implemented using supervised and unsupervised learning strategies. In both models, Arabic tweets were preprocessed first then various schemes of bag-of-N-grams were extracted to be used as features. The final submission was selected upon the best performance achieved by the supervised learning-based model. However, the results obtained by the unsupervised learning-based model are considered promising and evolvable if more rich lexica are adopted in further work.

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Churn Identification in Microblogs using Convolutional Neural Networks with Structured Logical Knowledge
Mourad Gridach | Hatem Haddad | Hala Mulki
Proceedings of the 3rd Workshop on Noisy User-generated Text

For brands, gaining new customer is more expensive than keeping an existing one. Therefore, the ability to keep customers in a brand is becoming more challenging these days. Churn happens when a customer leaves a brand to another competitor. Most of the previous work considers the problem of churn prediction using the Call Detail Records (CDRs). In this paper, we use micro-posts to classify customers into churny or non-churny. We explore the power of convolutional neural networks (CNNs) since they achieved state-of-the-art in various computer vision and NLP applications. However, the robustness of end-to-end models has some limitations such as the availability of a large amount of labeled data and uninterpretability of these models. We investigate the use of CNNs augmented with structured logic rules to overcome or reduce this issue. We developed our system called Churn_teacher by using an iterative distillation method that transfers the knowledge, extracted using just the combination of three logic rules, directly into the weight of the DNNs. Furthermore, we used weight normalization to speed up training our convolutional neural networks. Experimental results showed that with just these three rules, we were able to get state-of-the-art on publicly available Twitter dataset about three Telecom brands.