Rich morphology based N-gram language models for Arabic
Ahmad Emami, Imed Zitouni, et al.
INTERSPEECH 2008
This paper presents a fully statistical approach to Arabic mention detection and chaining system, built around the maximum entropy principle. The presented system takes a cascade approach to processing an input document, by first detecting mentions in the document and then chaining the identified mentions into entities. Both system components use a common maximum entropy framework, which allows the integration of a large array of feature types, including lexical, morphological, syntactic, and semantic features. Arabic offers additional challenges for this task (when compared with English, for example), as segmentation is a needed processing step, so one can correctly identify and resolve enclitic pronouns. The system presented has obtained very competitive performance in the automatic content extraction (ACE) evaluation program. © 2009 IEEE
Ahmad Emami, Imed Zitouni, et al.
INTERSPEECH 2008
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