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Toward an amazigh language processing

Since antiquity, the Amazigh heritage is expanding from generation to generation. In the aim of safeguarding it from being threatened of disappearance, it seems opportune to equip this language of necessary means to confront the stakes of access to the domain of New Information and Communication Technologies (ICT). In this context, and in the perspective to build tools and linguistic resources for the automatic processing of Amazigh language, we develop a lexicon and morphological rules using finite state technology within the linguistic developmental environment Nooj to parse amazigh texts.


Introduction
The Amazigh language in Morocco is considered as a prominent constituent of the Moroccan culture and this by its richness and originality. However it has been long discarded otherwise neglected as a source of enrichment cultural. Nevertheless, due to the creation of the Royal Institute of Amazigh Culture (IRCAM) 1 , this language has been introduced in the public domain including administration, media also in the educational system in collaboration with ministries. It has enjoyed its proper coding in the Unicode Standard (Andries, 2008;Zenkouar, 2008), an official spelling (Ameur et al., 2006a), appropriate standards for keyboard realization and linguistic structures that are being developed with a phased approach (Ameur et al., 2006b;Boukhris et al., 2008). This process was initiated by the standardization, vocabularies construction (Kamel, 2006;Ameur et al., 2009a;Ameur et al., 2006a;Ameur et al., 2009b), Alphabetical Arrangement (Outahajala, 2007), spelling standardization (Ameur et al., 2006a) and development of rules grammar (Boukhris et al., 2008).
However, this not sufficient for a less-resourced language (Berment, 2004) as the Amazigh to join the well-resourced language in information and Communication Technologies, mainly due to the lack of already available language processing resources and tools. Therefore, a set of scientific and linguistic research are undertaken to remedy to the current situation. These researches are divided, on the one hand, on researches that are concentrated on optical character recognition (OCR) (Amrouch et al., 2010;Es Saady et al., 2010;Fakir et al., 2009), and in the other hand, on those that are focused on natural language processing (Iazzi and Outahajala, 2008;Ataa Allah and Jaa, 2009;Boulaknadel, 2009;Ataa Allah and Boulaknadel, 2010;Outahajala et al., 2010;Boulaknadel and Ataa Allah, 2011), which constitute the priority components of researches.
In this context, the present work deals with ongoing research efforts to build tools and linguistic resources for the Amazigh language. Our first main objective is to develop a morphological analyzer to parse amazigh texts. For this purpose, we begin by building a morphological analyzer for Amazigh nouns, implemented using the Finite State Technology within the linguistic developmental environment Nooj. This paper is structured around five main sections: the first present a description of the Amazigh language particularities. The second expose the automatic Amazigh language processing, which includes an overview of NooJ environment, and the formalization of a set of rules. While the last section is dedicated to the conclusion and perspectives.

Amazigh language particularities
The Amazigh language also known as Berber or Tamazight (ⵜ ⴰ ⵎ ⴰ ⵣ ⵉ ⵖ ⵜ [tamazight]), is belonged to the African branch of the Afro-Asiatic language family, also referred to as Hamito-Semitic in the literature (Greenberg, 1966;Ouakrim, 1995). It is currently presented in a dozen countries ranging from Morocco, with 50% of the overall population 2 (Boukous, 1995), to Egypt, passing through Algeria with 25%, the Tunisia, Mauritania, Libya, Niger and the Mali (Chaker, 2003).
In Morocco, we distinguish between three major Amazigh dialects. Tarifit is spoken in northern Morocco, Tamazight in the Middle Atlas and south-eastern Morocco, and Tashelhit in southwestern Morocco and the High Atlas.
Today, the current situation of the Amazigh language is at a pivotal point. It holds co-official status in Morocco. Its morphology as lexical standardization process is still underway. At present, it represents the model taught in must schools and used on media and official papers published in Morocco.

Amazigh morphology
The Amazigh language presents a rich and complex morphology whose words can be classified into three morphosyntactic categories which we cite: the noun, the verb and particles (Boukhris et al., 2008;Ameur et al., 2004b). Practically, nouns and verbs are the base of the Amazigh morphology and the more important categories to focus on, as others can be derived from them. In this paper, we are interested in noun morphology.

Noun characteristics
The noun in the Amazigh language is always composed of one word between two spaces and formed from a root and a pattern. It is characterized by gender (masculine or feminine), number (singular or plural), and state (free or construct) (Boukhris et al., 2008).
Gender: the Amazigh noun is characterized by one of grammatical gender: masculine or feminine.
 The masculine noun: begins with one of the initial vowels: However, there are some exceptions as:  (Silberztein, 2007), is a linguistic development platform that provides a set of tools and methodologies for formalizing and developing a set of Natural Language Processing (NLP) applications. It presents a package of finite state tools that integrates a broad spectrum of computational technology from finite state automata to augmented/recursive transition networks. Thus, it presents a complete platform for formalizing various types of textual phenomena (orthography, lexical and productive morphology, local, structural and transformational syntax). For each of these formalization levels, NooJ propose a methodology, one or more formalisms, tools, software development and a corresponding parser that can be used to test each piece of the linguistic formalization over large corpora.
Given these advantages, we have undertaken to adopt NooJ for formalization, description and analysis of Amazigh language for building a module for that language. We begin our work by the formalization of the Amazigh language vocabulary. This formalization is described and stored into inflectional grammars, and can recognize all the corresponding inflected forms. To test these grammars, we built an electronic dictionary in which the lexical entries are attached to a set of linguistic information automatically generate using inflectional grammars which will be used for lexical analysis of texts.

Development of the lexicon
As part of developing a NooJ module for the Amazigh language, we elaborate our dictionary for Amazigh nouns based on a set of lexicons: Taifi dictionary (Taifi, 1988), amazigh vocabulary (Ameur et al., 2006b), and vocabulary of media (Ameur et al., 2009a).Our dictionary contains, currently, 5210 lexical entries which consist of: 4542 simple nouns, 424 proper nouns, 200 Noninflected nouns and 44 numerals which are given with their plural form, feminine correspondent and annexation state. Our inflected dictionary, calculated after the compilation of the dictionary, encounters 19,597 entries. Thus, we get from each lexical entry all forms related to it.

Morphological rules implementation
This study presents the formalization of the noun category in the NooJ platform. For this, a set of rules has been defined allowing to generate from a each entry, its inflectional information: gender, number and state.
The formalization is based on the use of certain generic commands predefined such as: To formalize the gender we built this rule that generates from a masculine entry its feminine correspondent. The rule is to add the discontinuous morpheme ⵜ [t] at the beginning and at the end of the noun.
The rules in Nooj Explanation Examples <LW>ⵜ <RW>ⵜ /f+s This rule adds "ⵜ " at the beginning and at the end.

 Number
For the amazigh plural, we have many plural forms which are generally unpredictable due to Amazigh complex morphology. To formalize these plural types, we have have relied on the works of Boukhris (Boukhris et al., 2008) and those of Oulhaj (Oulhaj, 2000). We searched formal rules to unify the calculation of plural forms. According to these works and to an heuristic study of the nouns in the Taifi dictionary and those of amazigh language vocabulary, we have raised, at this moment, 303 classes which 97 classes is for the external plural, 99 for the broken plural, 104 for the mixed plural and 3 classes for the plural in ⵉ ⴷ [id]. Each word could be associated with, at least, one flexional class. Thereafter, we provided some examples of rules for each of plural types.
-The external plural The rules in Nooj Explanation Examples <LW>ⵉ <S><RW>ⵜ ⵏ /m+p The initial vowel is transformed into ⵉ and the suffix ⵜ ⵏ [tn] is add at the end of the noun.

Evaluation
We conducted our experimentation on a sample of texts for story children. By applying our morphological grammars and our dictionary on text, we obtained the following results: The text contains 778 nouns. After performing lexical analysis we identified a total of 686 occurrences of nouns recognized and well annotated (1837 annotations). However, a total of 92 occurrences expressing unknowns nouns. This experiment shows that only 8% of the unknowns nouns that do not belong to our dictionary.

Conclusion and future works
In this paper, we try to restore the Amazigh language and culture and give it more visibility nationally and internationally through developing tools and resources necessary for its computational processing. Our aim to work on a morphological analyzer for Amazigh came from this scarcity of computational framework, a morphological analyzer being one of the fundamental tools in many NLP tasks. However, we build a morphological analyzer for Amazigh nouns, implemented using the Finite State Technology within the linguistic developmental environment Nooj. Amazigh morphological analyzer for nouns is an underway work and further development must be performed to make it a complete one. However, our analyzer achieves over 92% correct results in the analysis of 778 nouns extracted from the corpus.
For future work we planed to: -Enlarge the lexicon to include nouns from other dialects, -Include other part of speech in the morphological analyzer, -Construct a corpus of texts to evaluate the out-of-vocabulary rate of our dictionary.