Sakhar Alkhereyf


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Evaluating ChatGPT and Bard AI on Arabic Sentiment Analysis
Abdulmohsen Al-Thubaity | Sakhar Alkhereyf | Hanan Murayshid | Nouf Alshalawi | Maha Omirah | Raghad Alateeq | Rawabi Almutairi | Razan Alsuwailem | Manal Alhassoun | Imaan Alkhanen
Proceedings of ArabicNLP 2023

Large Language Models (LLMs) such as ChatGPT and Bard AI have gained much attention due to their outstanding performance on a range of NLP tasks. These models have demonstrated remarkable proficiency across various languages without the necessity for full supervision. Nevertheless, their performance in low-resource languages and dialects, like Arabic dialects in comparison to English, remains to be investigated. In this paper, we conduct a comprehensive evaluation of three LLMs for Dialectal Arabic Sentiment Analysis: namely, ChatGPT based on GPT-3.5 and GPT-4, and Bard AI. We use a Saudi dialect Twitter dataset to assess their capability in sentiment text classification and generation. For classification, we compare the performance of fully fine-tuned Arabic BERT-based models with the LLMs in few-shot settings. For data generation, we evaluate the quality of the generated new sentiment samples using human and automatic evaluation methods. The experiments reveal that GPT-4 outperforms GPT-3.5 and Bard AI in sentiment analysis classification, rivaling the top-performing fully supervised BERT-based language model. However, in terms of data generation, compared to manually annotated authentic data, these generative models often fall short in producing high-quality Dialectal Arabic text suitable for sentiment analysis.


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AraNPCC: The Arabic Newspaper COVID-19 Corpus
Abdulmohsen Al-Thubaity | Sakhar Alkhereyf | Alia O. Bahanshal
Proceedinsg of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection

This paper introduces a corpus for Arabic newspapers during COVID-19: AraNPCC. The AraNPCC corpus covers 2019 until 2021 via automatically-collected data from 12 Arab countries. It comprises more than 2 billion words and 7.2 million texts alongside their metadata. AraNPCC can be used for several natural language processing tasks, such as updating available Arabic language models or corpus linguistics tasks, including language change over time. We utilized the corpus in two case studies. In the first case study, we investigate the correlation between the number of officially reported infected cases and the collective word frequency of “COVID” and “Corona.” The data shows a positive correlation that varies among Arab countries. For the second case study, we extract and compare the top 50 keywords in 2020 and 2021 to study the impact of the COVID-19 pandemic on two Arab countries, namely Algeria and Saudi Arabia. For 2020, the data shows that the two countries’ newspapers strongly interacted with the pandemic, emphasizing its spread and dangerousness, and in 2021 the data suggests that the two countries coped with the pandemic.

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CAraNER: The COVID-19 Arabic Named Entity Corpus
Abdulmohsen Al-Thubaity | Sakhar Alkhereyf | Wejdan Alzahrani | Alia Bahanshal
Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)

Named Entity Recognition (NER) is a well-known problem for the natural language processing (NLP) community. It is a key component of different NLP applications, including information extraction, question answering, and information retrieval. In the literature, there are several Arabic NER datasets with different named entity tags; however, due to data and concept drift, we are always in need of new data for NER and other NLP applications. In this paper, first, we introduce Wassem, a web-based annotation platform for Arabic NLP applications. Wassem can be used to manually annotate textual data for a variety of NLP tasks: text classification, sequence classification, and word segmentation. Second, we introduce the COVID-19 Arabic Named Entities Recognition (CAraNER) dataset. CAraNER has 55,389 tokens distributed over 1,278 sentences randomly extracted from Saudi Arabian newspaper articles published during 2019, 2020, and 2021. The dataset is labeled by five annotators with five named-entity tags, namely: Person, Title, Location, Organization, and Miscellaneous. The CAraNER corpus is available for download for free. We evaluate the corpus by finetuning four BERT-based Arabic language models on the CAraNER corpus. The best model was AraBERTv0.2-large with 0.86 for the F1 macro measure.


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Email Classification Incorporating Social Networks and Thread Structure
Sakhar Alkhereyf | Owen Rambow
Proceedings of the Twelfth Language Resources and Evaluation Conference

Existing methods for different document classification tasks in the context of social networks typically only capture the semantics of texts, while ignoring the users who exchange the text and the network they form. However, some work has shown that incorporating the social network information in addition to information from language is effective for various NLP applications including sentiment analysis, inferring user attributes, and predicting inter-personal relations. In this paper, we present an empirical study of email classification into “Business” and “Personal” categories. We represent the email communication using various graph structures. As features, we use both the textual information from the email content and social network information from the communication graphs. We also model the thread structure for emails. We focus on detecting personal emails, and we evaluate our methods on two corpora, only one of which we train on. The experimental results reveal that incorporating social network information improves over the performance of an approach based on textual information only. The results also show that considering the thread structure of emails improves the performance further. Furthermore, our approach improves over a state-of-the-art baseline which uses node embeddings based on both lexical and social network information.


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Morphologically Annotated Corpora for Seven Arabic Dialects: Taizi, Sanaani, Najdi, Jordanian, Syrian, Iraqi and Moroccan
Faisal Alshargi | Shahd Dibas | Sakhar Alkhereyf | Reem Faraj | Basmah Abdulkareem | Sane Yagi | Ouafaa Kacha | Nizar Habash | Owen Rambow
Proceedings of the Fourth Arabic Natural Language Processing Workshop

We present a collection of morphologically annotated corpora for seven Arabic dialects: Taizi Yemeni, Sanaani Yemeni, Najdi, Jordanian, Syrian, Iraqi and Moroccan Arabic. The corpora collectively cover over 200,000 words, and are all manually annotated in a common set of standards for orthography, diacritized lemmas, tokenization, morphological units and English glosses. These corpora will be publicly available to serve as benchmarks for training and evaluating systems for Arabic dialect morphological analysis and disambiguation.


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Unified Guidelines and Resources for Arabic Dialect Orthography
Nizar Habash | Fadhl Eryani | Salam Khalifa | Owen Rambow | Dana Abdulrahim | Alexander Erdmann | Reem Faraj | Wajdi Zaghouani | Houda Bouamor | Nasser Zalmout | Sara Hassan | Faisal Al-Shargi | Sakhar Alkhereyf | Basma Abdulkareem | Ramy Eskander | Mohammad Salameh | Hind Saddiki
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


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Work Hard, Play Hard: Email Classification on the Avocado and Enron Corpora
Sakhar Alkhereyf | Owen Rambow
Proceedings of TextGraphs-11: the Workshop on Graph-based Methods for Natural Language Processing

In this paper, we present an empirical study of email classification into two main categories “Business” and “Personal”. We train on the Enron email corpus, and test on the Enron and Avocado email corpora. We show that information from the email exchange networks improves the performance of classification. We represent the email exchange networks as social networks with graph structures. For this classification task, we extract social networks features from the graphs in addition to lexical features from email content and we compare the performance of SVM and Extra-Trees classifiers using these features. Combining graph features with lexical features improves the performance on both classifiers. We also provide manually annotated sets of the Avocado and Enron email corpora as a supplementary contribution.