Multi-Level Analysis and Annotation of Arabic Corpora for Text-to-Sign Language MT

The Arabic language is morphologically rich and syntactically complex with many differences from European languages, and this creates a challenge when porting existing annotation tools to Arabic. In this paper, we present an ongoing effort in lexical semantic analysis and annotation of Modern Standard Arabic (MSA) text, a semi automatic annotation tool concerned with the morphologic, syntactic, and semantic levels of description. Besides the aim of providing a multi-level annotation tool for Arabic corpora, our goals are (1) to investigate the suitability of Frame Semantics (FS) approach (Fillmore 1985) for representing and analysing Arabic text (2) to provide corpus-attested linguistics materials for frame-based contrastive text analysis between Arabic and English in terms of lexicalization patterns; (3) to automatically derive mappings rules from annotated sentences. Such corpus-attested mapping rules between linguistic form and its meaning can support semantic analysis in knowledge-based NLP systems such as machine translation, information extraction etc. Following syntactically-based annotation projects for English, serious attempts have been made to annotate Arabic corpora, such as the Penn Arabic Treebank (PATB), (Maamouri et al. 2004) and the Quranic Arabic Dependency Treebank (QADT) (Dukes et al. 2010). However, semantically-based annotation for Arabic corpora has not yet been garnering the same attention. Our semantic representations are based upon use of frame-semantic paradigm; it is actually used in a MT system from Arabic to Algerian Sign Language aimed to assist deaf children and in order to bridge the gap between Arabic written texts and Algerian Sign Language (Lakhfif and Laskri 2010a,b, 2011). Annotation outputs are available in XML format compatible with the FrameNet project (Fillmore and Petruck 2003) design and can be portable to other NLP systems.