ISO standard modeling of a large Arabic dictionary

Abstract

In this paper, we address the problem of the large coverage dictionaries of Arabic language usable both for direct human reading and automatic Natural Language Processing. For these purposes, we propose a normalized and implemented modeling, based on Lexical Markup Framework (LMF-ISO 24613) and Data Registry Category (DCR-ISO 12620), which allows a stable and well-defined interoperability of lexical resources through a unification of the linguistic concepts. Starting from the features of the Arabic language, and due to the fact that a large range of details and refinements need to be described specifically for Arabic, we follow a finely structuring strategy. Besides its richness in morphology, syntax and semantics knowledge, our model includes all the Arabic morphological patterns to generate the inflected forms from a given lemma and highlights the syntactic-semantic relations. In addition, an appropriate codification has been designed for the management of all types of relationships among lexical entries and their related knowledge. According to this model, a dictionary named El Madar 1 has been built and is now publicly available on line. The data are managed by a user-friendly Web-based lexicographical workstation. This work has not been done in isolation, but is the result of a collaborative effort by an international team mainly within the ISO network during a period of eight years. A. Khemakhem et al.is to merge them in order to obtain a new richer resource. More generally, the exchange remains a difficult (and expensive) issue when nothing has been scheduled for this purpose. To meet this challenge, several projects were conducted such as ACQUILEX (Bogurev et al. These projects led to the emergence of the LMF (Lexical Markup Framework) ISO standard for the lexical structure modeling (ISO 24613) (Francopoulo 2003), (Francopoulo and George 2008) in association with the ISO Data Categories Registry (DCR) 3 following ISO 12620 (Ide and Romary 2004). These standards were designed by a group of sixty ISO experts coming from different cultures, languages and continents. Numerous developments followed in different parts of the world. 4 Unfortunately, the Arabic language did not immediately benefit from the emergence of these standards, although it is spoken by more than 300 million people around the world, and is the official language of more than twenty countries. The language still uses references to different printed dictionaries based on incompatible lexicographical schools. Only few works tried the application of LMF on the Arabic language out, according to previous revisions of this standard. Some developments were made in morphology (Khemakhem, Gargouri and Abdelwahed 2006), (Romary, Salmon-Alt and Francopoulo 2004), (Salmon-Alt, Akrout and Romary 2005) and some studies were conducted in syntax (Loukil, Haddar and Ben Hamadou 2008). However, these works were developed during the drafting of the LMF standard and were not updated according to the ISO validation.Obviously, the situation of the Arabic lexica...

Enhancing the sentence similarity measure by semantic and syntactico-semantic knowledge

“…Experiments use on the one hand the LMF standardized Arabic dictionary [10] as a resource to exploit [10] the synonymy of words and properties of semantic arguments (semantic class and thematic role)and, on the other hand, the Stanford Parser [4], the MADAMIRA tool [16] to reduce words to their stem or lemma by removing the suffix, the prefix. After that, they match the remaining word with verbal or noun patterns and the Weka software package [5] to find out the optimal parameters in the learning phase.…”

“…Therefore, we believe that the enrichment of our database of Arabic sentences can significantly enhance the results. In addition, the performance of our system depends on the lemmatizer system, syntactical parser, synonyms and semantic predicates retrieved from the Arabic LMF dictionary [10]. According to a comparative evaluation study of Arabic language stemmers and syntactical parsers, MADAMIRA [16] and the Stanford parser [4] achieved the highest accuracy.…”

“…Elmadar 3 is an Arabic lexical resource that conforms to the LMF standard ISO-24613. The model of this dictionary [10] covers all lexical levels: morphological, syntactic, semantic and syntactico-semantic. This dictionary contains about 37,000 lexical entries, among which 10,800 are verbs and 3800 roots.…”

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“…However, these sentence similarity methods based on semantic information do not directly induce a real similarity score. For this reason, some approaches estimate the similarity between sentences based on syntactic and semantic information, called hybrid methods, such as [12] and [6] that take account of the semantic information and word order information implied in the sentence, [19] and syntactic dependency and [21] that takes account of synonymy relations between the word sense and the semantic predicate based on the LMF standardized Arabic dictionary [9]. Indeed, the authors estimate to compute the sentence similarity for Arabic Language.…”