Building a syntactic-semantic interface for a semi-automatically generated TAG for Arabic

Extended version of a paper presented at ACIT 2017. Full text available at https://iajit.org/portal/PDF/Special%20Issue%202018,%20No.%203A/17414.pdf; International audience; Syntactic and semantic resources play an important role for various Natural Language Processing (NLP) tasks by providing information about the correct structural representations of the sentences and their meaning. To date, there is not a wide-coverage electronic grammar for the Arabic language. In this context, we present a new approach for building a tree adjoining grammar to represent the syntax and the semantic of modern standard Arabic. This grammar is produced semi-automatically with the XMG (eXtensible MetaGrammar) description language. First the syntax of Arabic is described using the defined Arab-XMG meta-grammar. Then semantic information is added by introducing semantic frame-based dimension into the meta-grammar. This is achieved by exploiting lexical resources such as Arabic VerbNet. Finally, the link between semantic and syntax is established using a syntax-semantic interface that allows the construction of sentence meaning through semantic role labeling. Experiments were performed to check grammar coverage as well as the syntactic-semantic analysis. The results showed that the generated grammar can cover the basic syntactic structures of Arabic sentences and the different phrasal structures with a precision rate of about 92%. Moreover, it confirms the effectiveness of the proposed approach as we were able to parse semantically a set of sentences and build their semantic representations with a precision rate of about 72%.