Ryan Roth

Also published as: Ryan M. Roth


2014

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MADAMIRA: A Fast, Comprehensive Tool for Morphological Analysis and Disambiguation of Arabic
Arfath Pasha | Mohamed Al-Badrashiny | Mona Diab | Ahmed El Kholy | Ramy Eskander | Nizar Habash | Manoj Pooleery | Owen Rambow | Ryan Roth
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this paper, we present MADAMIRA, a system for morphological analysis and disambiguation of Arabic that combines some of the best aspects of two previously commonly used systems for Arabic processing, MADA (Habash and Rambow, 2005; Habash et al., 2009; Habash et al., 2013) and AMIRA (Diab et al., 2007). MADAMIRA improves upon the two systems with a more streamlined Java implementation that is more robust, portable, extensible, and is faster than its ancestors by more than an order of magnitude. We also discuss an online demo (see http://nlp.ldeo.columbia.edu/madamira/) that highlights these aspects.

2013

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Morphological Analysis and Disambiguation for Dialectal Arabic
Nizar Habash | Ryan Roth | Owen Rambow | Ramy Eskander | Nadi Tomeh
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Automatic Morphological Enrichment of a Morphologically Underspecified Treebank
Sarah Alkuhlani | Nizar Habash | Ryan Roth
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Overview of the SPMRL 2013 Shared Task: A Cross-Framework Evaluation of Parsing Morphologically Rich Languages
Djamé Seddah | Reut Tsarfaty | Sandra Kübler | Marie Candito | Jinho D. Choi | Richárd Farkas | Jennifer Foster | Iakes Goenaga | Koldo Gojenola Galletebeitia | Yoav Goldberg | Spence Green | Nizar Habash | Marco Kuhlmann | Wolfgang Maier | Joakim Nivre | Adam Przepiórkowski | Ryan Roth | Wolfgang Seeker | Yannick Versley | Veronika Vincze | Marcin Woliński | Alina Wróblewska | Eric Villemonte de la Clergerie
Proceedings of the Fourth Workshop on Statistical Parsing of Morphologically-Rich Languages

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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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Reranking with Linguistic and Semantic Features for Arabic Optical Character Recognition
Nadi Tomeh | Nizar Habash | Ryan Roth | Noura Farra | Pradeep Dasigi | Mona Diab
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2011

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Using Deep Morphology to Improve Automatic Error Detection in Arabic Handwriting Recognition
Nizar Habash | Ryan Roth
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2009

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CATiB: The Columbia Arabic Treebank
Nizar Habash | Ryan Roth
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

2008

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Arabic Morphological Tagging, Diacritization, and Lemmatization Using Lexeme Models and Feature Ranking
Ryan Roth | Owen Rambow | Nizar Habash | Mona Diab | Cynthia Rudin
Proceedings of ACL-08: HLT, Short Papers

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Identification of Naturally Occurring Numerical Expressions in Arabic
Nizar Habash | Ryan Roth
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

In this paper, we define the task of Number Identification in natural context. We present and validate a language-independent semi-automatic approach to quickly building a gold standard for evaluating number identification systems by exploiting hand-aligned parallel data. We also present and extensively evaluate a robust rule-based system for number identification in natural context for Arabic for a variety of number formats and types. The system is shown to have strong performance, achieving, on a blind test, a 94.8% F-score for the task of correctly identifying number expression spans in natural text, and a 92.1% F-score for the task of correctly determining the core numerical value.