Improved Seo Apl development for Georgian language using natural language processing method: Using tecnilogical advances of obtimize the content of Georgian text
Ekaterine Papava, Tamar Lominadze, Rusudan Papiashvili · Works of Georgian Technical University · 2024
This article delves into the intricate intersection of Natural Language Processing (NLP) and Search Engine Optimization (SEO) specifically tailored for the complexities of the Georgian language. Addressing challenges arising from its intricate morphological structure and unique script, the discussion encompasses Named Entity Recognition (NER), tokenization, homonym recognition, and more. Emphasizing the scarcity of labeled datasets in Georgian, the article underscores the necessity for NLP solutions from an SEO perspective and highlights opportunities brought about by digitizing the language. Overcoming obstacles, the article underscores the considerable potential within NLP, envisioning a future where our API seamlessly embeds NER in SEO, revolutionizing the evaluation of digital content. This development promises to significantly enhance search response materials, user experiences on websites, and overall engagement within the Georgian digital space, establishing a benchmark for high-quality data. The content further explores the intricacies of keyword research and the challenges associated with backlink analysis in the unique context of the Georgian language. Ultimately, it serves as an exploration of the potential fusion of artificial intelligence and SEO methods, creating a seamless synergy of technological innovation and linguistic understanding. This integration aims to cultivate a richer and more interactive digital environment for Georgian content.