Extracting Knowledge from an Arabic-English Machine-Readable Dictionary Using Information Extraction
arXiv:2606.28457v1 Announce Type: new Abstract: Natural language processing (NLP) applications need large and rich amount of linguistic knowledge. Furthermore, electronic language sources such as dictionaries, encyclopedia, and corpora became available. So, automatic methods are emerged to extract lexical information from those sources to overcome the knowledge acquisition bottleneck. We presented a method to automatically extract lexical information from a machine-readable version of the Arabic-English Al-Mawrid dictionary. We used n-gram analysis and key-word-in-context (KWIC) analysis to di
The proliferation of digital linguistic resources and the increasing demand for NLP applications, particularly for less-resourced languages, drives the need for automated knowledge extraction methods.
This development allows for more efficient and scalable acquisition of linguistic knowledge from existing resources, reducing bottlenecks in developing NLP for languages like Arabic, and furthering applications across various sectors.
The reliance on manual annotation for building linguistic resources is reduced, enabling faster development cycles for NLP systems that require large and rich linguistic knowledge bases.
- · AI developers (especially for less-resourced languages)
- · Academia (linguistics, NLP)
- · Governments (for language preservation/processing)
- · Arabic-speaking communities
- · Manual lexicographers (in the long term)
Improved performance and broader accessibility of NLP tools for Arabic.
Accelerated development of AI agents and services tailored for Arabic speakers and content.
Enhanced digital inclusion and economic opportunities for Arabic-speaking populations through advanced language technologies.
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