Mohamed Altantawy

Also published as: Mohamed AlTantawy


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Summarizing Relationships for Interactive Concept Map Browsers
Abram Handler | Premkumar Ganeshkumar | Brendan O’Connor | Mohamed AlTantawy
Proceedings of the 2nd Workshop on New Frontiers in Summarization

Concept maps are visual summaries, structured as directed graphs: important concepts from a dataset are displayed as vertexes, and edges between vertexes show natural language descriptions of the relationships between the concepts on the map. Thus far, preliminary attempts at automatically creating concept maps have focused on building static summaries. However, in interactive settings, users will need to dynamically investigate particular relationships between pairs of concepts. For instance, a historian using a concept map browser might decide to investigate the relationship between two politicians in a news archive. We present a model which responds to such queries by returning one or more short, importance-ranked, natural language descriptions of the relationship between two requested concepts, for display in a visual interface. Our model is trained on a new public dataset, collected for this task.


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Using Simple NLP Tools to Trace the Globalization of the Art World
Mohamed AlTantawy | Alix Rule | Owen Rambow | Zhongyu Wang | Rupayan Basu
Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science


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DIRA: Dialectal Arabic Information Retrieval Assistant
Arfath Pasha | Mohammad Al-Badrashiny | Mohamed Altantawy | Nizar Habash | Manoj Pooleery | Owen Rambow | Ryan M. Roth | Mona Diab
The Companion Volume of the Proceedings of IJCNLP 2013: System Demonstrations


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Fast Yet Rich Morphological Analysis
Mohamed Altantawy | Nizar Habash | Owen Rambow
Proceedings of the 9th International Workshop on Finite State Methods and Natural Language Processing


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Morphological Analysis and Generation of Arabic Nouns: A Morphemic Functional Approach
Mohamed Altantawy | Nizar Habash | Owen Rambow | Ibrahim Saleh
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

MAGEAD is a morphological analyzer and generator for Modern Standard Arabic (MSA) and its dialects. We introduced MAGEAD in previous work with an implementation of MSA and Levantine Arabic verbs. In this paper, we port that system to MSA nominals (nouns and adjectives), which are far more complex to model than verbs. Our system is a functional morphological analyzer and generator, i.e., it analyzes to and generates from a representation consisting of a lexeme and linguistic feature-value pairs, where the features are syntactically (and perhaps semantically) meaningful, rather than just morphologically. A detailed evaluation of the current implementation comparing it to a commonly used morphological analyzer shows that it has good morphological coverage with precision and recall scores in the 90s. An error analysis reveals that the majority of recall and precision errors are problems in the gold standard or a result of the discrepancy between different models of form-based/functional morphology.