AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
An unsupervised system that can automatically generate Arabic semantic fields from a large text corpus that achieves 78.17% accuracy in determining the appropriate contextual meaning of homographs in the contemporary Arabic system is developed.
A supervised method for Hindi word sense disambiguation is employed, incorporating contextual feature modeling and systematic preprocessing to effectively resolve ambiguity.
LLM-assisted sense assignment with a Serbian WordNet-based custom inventory, iterative inventory expansion, and expert validation is combined with a constrained JSON-formatted output to support the practical construction and refinement of sense-annotated resources in a low-resource setting.
Arabic NER and NED research is limited by the lack of large-scale public datasets that support both tasks with validated entity links and difficulty-aware evaluation. In this paper, we propose ANERD, a large-scale Arabic corpus and benchmark for NER/NED, to overcome these drawbacks. ANERD includes 502,756 sentences fro...
A baseline morphological analyzer is built by evaluating a wider range of supervised systems, including lexicon-lookup, Conditional Random Fields (CRFs), full fine-tuning of Arabic PLMs, and AraT5 configurations, to demonstrate severe limitations in current capabilities.
The findings underscore the potential of advanced NLP techniques to overcome language-specific challenges, providing a foundation for future research in multilingual plagiarism detection and enhancing the development of tools for other languages facing similar challenges.
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