Exploiting Functional Discourse Grammar to Enhance Complex Arabic Relation Extraction using a Hybrid Semantic Knowledge Base - Machine Learning Approach

OSMAN, TAHA; KHALIL, HUSSEIN; MILTAN, MOHAMMED; SHAALAN, KHALED; ALFRJANI, ROWIDA

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Exploiting Functional Discourse Grammar to Enhance Complex Arabic Relation Extraction using a Hybrid Semantic Knowledge Base - Machine Learning Approach

Date

2023

Authors

Abstract

Relation extraction from unstructured Arabic text is especially challenging due to the Arabic language com plex morphology and the variation in word semantics and lexical categories. The research documented in this paper presents a hybrid Semantic Knowledge base - Machine Learning (SKML) approach for extracting complex Arabic relations from unstructured Arabic documents; the proposed approach exploits the princi ples of Functional Discourse Grammar (FDG) to emphasise the semantic and pragmatic properties of the language and facilitate the identification of relation elements. At the initial phase, the novel FDG-SKML re lation extraction approach deploys a lexical-based mechanism that utilises a purposely built domain-specific Semantic Knowledge to encode the semantic association between the identified relations’ elements. The eval uation of the initial stage evidenced improved accuracy for extracting most complex Arabic relations. The initial relation extraction mechanism was further extended by integrating its output into a Machine Learn ing classifier that facilitated extracting especially complex relations with significant disparity in the relation elements’ presence, order, and correlation. Using Economics as the problem domain, experimental evalua tion evidenced the high accuracy of our FDG-SKML approach in complex Arabic relation extraction task and demonstrated its further improvement upon integration with machine learning classifiers.