Violetta Cavalli-Sforza


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Feature Optimization for Predicting Readability of Arabic L1 and L2
Hind Saddiki | Nizar Habash | Violetta Cavalli-Sforza | Muhamed Al Khalil
Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications

Advances in automatic readability assessment can impact the way people consume information in a number of domains. Arabic, being a low-resource and morphologically complex language, presents numerous challenges to the task of automatic readability assessment. In this paper, we present the largest and most in-depth computational readability study for Arabic to date. We study a large set of features with varying depths, from shallow words to syntactic trees, for both L1 and L2 readability tasks. Our best L1 readability accuracy result is 94.8% (75% error reduction from a commonly used baseline). The comparable results for L2 are 72.4% (45% error reduction). We also demonstrate the added value of leveraging L1 features for L2 readability prediction.


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Developing ARET: An NLP-based Educational Tool Set for Arabic Reading Enhancement
Mohammed Maamouri | Wajdi Zaghouani | Violetta Cavalli-Sforza | Dave Graff | Mike Ciul
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP


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Improving example-based machine translation through morphological generalization and adaptation
Aaron B. Phillips | Violetta Cavalli-Sforza | Ralf D. Brown
Proceedings of Machine Translation Summit XI: Papers

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Proceedings of the 2007 Workshop on Computational Approaches to Semitic Languages: Common Issues and Resources
Violetta Cavalli-Sforza | Imed Zitouni
Proceedings of the 2007 Workshop on Computational Approaches to Semitic Languages: Common Issues and Resources

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An Arabic Slot Grammar Parser
Michael McCord | Violetta Cavalli-Sforza
Proceedings of the 2007 Workshop on Computational Approaches to Semitic Languages: Common Issues and Resources


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IMORPHĒ: An Inheritance and Equivalence Based Morphology Description Compiler
Violetta Cavalli-Sforza | Abdelhadi Soudi
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

IMORPHĒ is a significantly extended version of MORPHE, a morphology description compiler. MORPHE’s morphology description language is based on two constructs: 1) a morphological form hierarchy, whose nodes relate and differentiate surface forms in terms of the common and distinguishing inflectional features of lexical items; and 2) transformational rules, attached to leaf nodes of the hierarchy, which generate the surface form of an item from the base form stored in the lexicon. While MORPHE’s approach to morphology description is intuitively appealing and was successfully used for generating the morphology of several European languages, its application to Modern Standard Arabic yielded morphological descriptions that were highly complex and redundant. Previous modifications and enhancements attempted to capture more elegantly and concisely different aspects of the complex morphology of Arabic, finding theoretical grounding in Lexeme-Based Morphology. Those extensions are being incorporated in a more flexible and less ad hoc fashion in IMORPHE, which retains the unique features of our previous work but embeds them in an inheritance-based framework in order to achieve even more concise and modular morphology descriptions and greater runtime efficiency, and lays the groundwork for IMORPHE to become an analyzer as well as a generator.


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Challenges in using an example-based MT system for a transnational digital government project
Violetta Cavalli-Sforza | Ralf D. Brown | Jaime G. Carbonell | Peter G. Jansen | Jae Dong Kim
Proceedings of the 9th EAMT Workshop: Broadening horizons of machine translation and its applications

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Developing Language Resources for a Transnational Digital Government System
Violetta Cavalli-Sforza | Jaime G. Carbonell | Peter J. Jansen
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)


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Challenges in adapting an interlingua for bidirectional English-Italian translation
Violetta Cavalli-Sforza | Krzysztof Czuba | Teruko Mitamura | Eric Nyberg
Proceedings of the Fourth Conference of the Association for Machine Translation in the Americas: Technical Papers

We describe our experience in adapting an existing high- quality, interlingual, unidirectional machine translation system to a new domain and bidirectional translation for a new language pair (English and Italian). We focus on the interlingua design changes which were necessary to achieve high quality output in view of the language mismatches between English and Italian. The representation we propose contains features that are interpreted differently, depending on the translation direction. This decision simplified the process of creating the interlingua for individual sentences, and allows the system to defer mapping of language-specific features (such as tense and aspect), which are realized when the target syntactic feature structure is created. We also describe a set of problems we encountered in translating modal verbs, and discuss the representation of modality in our interlingua.

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Arabic Morphology Generation Using a Concatenative Strategy
Violetta Cavalli-Sforza | Abdelhadi Soudi | Teruko Mitamura
1st Meeting of the North American Chapter of the Association for Computational Linguistics