Sakhar Alkhereyf


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Email Classification Incorporating Social Networks and Thread Structure
Sakhar Alkhereyf | Owen Rambow
Proceedings of the 12th 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.