Karima Meftouh

Cross-Dialectal Arabic Processing

We present, in this paper an Arabic multi-dialect study including dialects from both the Maghreb and the Middle-east that we compare to the Modern Standard Arabic (MSA). Three dialects from Maghreb are concerned by this study: two from Algeria and one from Tunisia and two dialects from Middle-east (Syria and Palestine). The resources which have been built from scratch have lead to a collection of a multi-dialect parallel resource. Furthermore, this collection has been aligned by hand with a MSA corpus. We conducted several analytical studies in order to understand the relationship between these vernacular languages. For this, we studied the closeness between all the pairs of dialects and MSA in terms of Hellinger distance. We also performed an experiment of dialect identification. This experiment showed that neighbouring dialects as expected tend to be confused, making difficult their identification. Because the Arabic dialects are different from one region to another which make the communication between people difficult, we conducted cross-lingual machine translation between all the pairs of dialects and also with MSA. Several interesting conclusions have been carried out from this experiment.

An Algerian dialect: Study and Resources

Arabic is the official language overall Arab coun-tries, it is used for official speech, news-papers, public adminis-tration and school. In Parallel, for everyday communication, non-official talks, songs and movies, Arab people use their dialects which are inspired from Standard Arabic and differ from one Arabic country to another. These linguistic phenomenon is called disglossia, a situation in which two distinct varieties of a language are spoken within the same speech community. It is observed Throughout all Arab countries, standard Arabic widely written but not used in everyday conversation, dialect widely spoken in everyday life but almost never written. Thus, in NLP area, a lot of works have been dedicated for written Arabic. In contrast, Arabic dialects at a near time were not studied enough. Interest for them is recent. First work for these dialects began in the last decade for middle-east ones. Dialects of the Maghreb are just beginning to be studied. Compared to written Arabic, dialects are under-resourced languages which suffer from lack of NLP resources despite their large use. We deal in this paper with Arabic Algerian dialect a non-resourced language for which no known resource is available to date. We present a first linguistic study introducing its most important features and we describe the resources that we created from scratch for this dialect.

Generation of the sense of a sentence in Arabic language with a connectionist approach

For several decades, research on natural language processing (NLP) has been dominated by the symbolic approach. However, during the last few years, there has been increasing interest in the connectionist technical applicability for NLP to converge towards connectionist NLP (CNLP). In this paper, we propose a connectionist model for the generation of an internal representation of the sense of a sentence in the Arabic language, based on semantic cases. We use the backpropagation algorithm in a simple recurrent network. A sentence is analyzed word by word. Every word is introduced to the network according to its semantic features. The task of the network consists of reading the sentence and deciding on a suitable semantic role for each word. The network successfully learned a case role assignment task. It has been experimented with on several corpora. The network has also been tested on a composed corpus of different sizes of sentences (2, 3, 4 and more), and a generalization rate approaching 92% was obtained.