Abstract

We build a model to parse the Arabic verbal sentence based on Arabic grammar ontology. The ontology conceptualizes the Arabic verbal sentence through the representation of grammar parsing classes, verb properties, and conjunction checking. By populating the ontology with verbal sentences and adding grammar rules, we form a verbal sentence knowledge base. The parsing model is supported by morphological analysis for sentence syntactic analysis and supported by Arabic synonyms extractor for deriving synonyms. We have implemented the model and have provided it with a user interface where the user can enter a sentence to be parsed and obtains the parsing results. The interface has the options to partially or totally add diacritics to the words of the sentence and it has the possibility to remove ambiguity by choosing the most appropriate analysis from lexicon results. To evaluate the model, we have selected a representative set of Arabic verbal sentences from Arabic grammar books that represent all the possibilities of a verbal sentence. We have performed several parsing tests on these sentences with and without diacritics. The results prove the ability of the model to parse the various forms of the verbal sentence. The accuracy increases when the sentence is diacriticized while avoiding free word order and following the Arabic verbal sentence general form.

Highlights

  • Parsing is necessary for distinguishing the meaning and understanding the intentions of a sentence and rolling out any ambiguities

  • The model receives a sentence as an input from the user, identifies if it is verbal by checking the first word of the sentence as it is a verb or not, starts defining the syntax of each word, and performs parsing of these words depending on Arabic Grammar Knowledge base

  • The User Interface accepts a sentence to be parsed from the user, and displays the results after performing the needed parsing steps based on the Arabic Grammar Ontology and the grammar rules

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Summary

INTRODUCTION

Parsing is necessary for distinguishing the meaning and understanding the intentions of a sentence and rolling out any ambiguities. When the ambiguity in the sentence is resolved, various possible interpretations are reduced and the sentence becomes more obvious Parsing such a morphologically rich and free word order languages is a challenging task, requiring advanced techniques in NLP for processing the words and requiring the machine to understand the syntactic and semantic analysis of the words. The model receives a sentence as an input from the user, identifies if it is verbal by checking the first word of the sentence as it is a verb or not (except if the verb is preceded with verb preposition), starts defining the syntax of each word, and performs parsing of these words depending on Arabic Grammar Knowledge base. Study the domain of Arabic grammar and Arabic parsing rules to extract the elements of the ontology including objects, properties, relations, instances

Parsing Model Development
Model Evaluation
RELATED WORK
A Natural Language Processing Methods
B Transition Networks Methods
C Machine Learning Methods
D Semantic Approaches
E Ontology-Based Approaches
A Arabic Grammar Knowledge Base
Properties
B The Morphological Analyzer
C The Synonym Extractor
D The Word Parser
E The User Interface
F Flow of the Model
IMPLEMENTING THE AVSP MODEL
EVALUATING THE AVSP MODEL
Findings
CONCLUSION
Full Text
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