Amazighe Verbal Inflectional Morphology: A New Approach for Analysis and Generation

Abstract

Amazighe inflectional morphology poses special challenges to Natural Language Processing (NLP) systems. Its rich morphology and the highly complex word formation process of roots and patterns make NLP tools for Amazighe very challenging. In this paper we present an approach for inflectional morphological analysis and generation for Amazighe verbs. The main motivation for this work is to obtain a linguistically motivated tool based on the concept of patterns and allows, from a verbal entries, to predict the inflectional forms. In this context, the present paper sheds light on three axes. The first axis deals with the levels of the new approach development. The second axis demonstrates the implementation, using finite state transducers, of the extracted rules. The fourth axis discusses experimental evaluations conducted to assess the performance of the analyzer. Our analyzer exploits the efficiency and flexibility offered by finite state machines in modeling while using the NooJ Finite State tools.