Arabic Psychological Verb Recognition Through NooJ Transformational Grammars

Complex Arabic sentences, especially those containing Arabic psychological verbs, follow a common underlying structure characterized by two essential components: the predicate and the subject. In addition, there are two optional elements: the head and the complement. These sentences, rooted in basic noun phrases (NPs), can be expanded within the predicate, subject, or complement, resulting in compound structures. This study aims to develop a syntactic analyzer for parsing complex sentences containing Arabic psychological verbs. To achieve this, we will use the dictionary generated from the lexicon-grammar table of Arabic psychological verbs, which contains all lexical, syntactic, semantic, and transformational information related to these verbs. Then, we will extend an existing analyzer to recognize and label all grammatical structures within complex sentences containing Arabic psychological verbs. Finally, we will evaluate the efficiency of this analyzer through tests on different texts and corpora.

Section: Methodsmentioning

confidence: 93%

Syntactic analysis of complex sentences containing Arabic psychological verbs

Amzali,

Kourtin,

Mourchid

et al. 2024

“…In the case of Arabic, El Hannach [17] pioneered this approach by studying psychological verbs, qualitative verbs [18], and support verbs [19]. In a parallel effort, Amzali et al [20] created lexicon-grammar tables for psychological verbs, studied their transformations [21], and their syntactic analysis [22]. In a related context, Kourtin et al [23] developed a program to generate NooJ dictionaries from lexicongrammar tables and created lexicon-grammar tables for modern Arabic frozen expressions [24].…”

Section: Introductionmentioning

confidence: 99%

Lexicon-grammar tables standardization and implementation

Kourtin,

Amzali,

Mourchid

et al. 2024

<span>The lexicon-grammar approach is a very important linguistic approach in automatic natural language processing (NLP). It allows for the description of the lexicon of the language through readable and intuitive tables for human manual editing. However, the automatic use of the lexicon-grammar tables in the automatic NLP platforms remains difficult, given the incompatibility between the codes used to represent the properties in the lexicon-grammar tables and those used to represent the properties in the automatic NLP platforms. In this work, we present our method of standardizing the lexicon-grammar tables for the French language, since they constitute very rich lexical, syntactic, and semantic linguistic resources. First, we standardize their properties so that they can be compatible with those used in the NLP platforms. Then, to implement the standardized tables, we used a linguistic platform such as NooJ. For that, we describe the process of integrating these tables into this platform through the automatic generation of the dictionaries from these tables. Finally, to test the efficiency of the generated dictionaries, we create for some of them syntactic grammars that take into account all the grammatical, syntactic, and semantic information contained in the dictionaries.</span>

“…Like rules have been used for the implementation of Arabic phonological rules [9], formalization of the Arabic grammatical category (V-a) [7], lexicon-grammar tables development for Arabic psychological verbs, applying transformational grammars to recognize Arabic psychological verbs [9]. works mentioned in [4]- [9] have been realized with great attention to the Arabic root and pattern morphology. Hence, developers can rely on these works to develop NLP applications.…”

mentioning

confidence: 99%

The use of Arabic linguistic resources to develop learning applications

Blanchete

Mourchid

2022

<span>This paper aims at explaining how Arabic linguistic resources are generated and exploited to enhance Arabic acquisition. We have adopted the root and pattern approach to generate our resources using the linguistic NooJ platform. This work has been carried out in two phases: generating the linguistic resources and developing the application that exploits the pre-built resources. First, we have generated three different resources: comprehensive verbs and masdar resources linked to each other. A nouns-and-adjectives resource, where nouns and adjectives are linked to their broken plural forms. NooJ calls these resources to apply linguistic analysis to a given corpus and returns detailed annotations, which provides accurate morphological, syntactical, and semantic properties of each analyzed word. We have also used the mixed nature of Arabic masdar to implement transformational rules, which generate nominal sentences from verbal ones and vice versa. Second, we have developed an application that provides valuable learning functionalities, like full/semi verb conjugation, the extraction of broken plural forms of a given singular form, the extraction of masdar forms of a given verb form, and return words that share the same root. The developed application can be used by teachers, students/learners, and computational linguists interested in Arabic acquisition.</span>