Abstract
The Internet has changed dramatically over the past decade, and search engines have worked hard to adapt to these changes by providing better content to users. Many efforts have been made to assess document quality, including attempts to use semantic analysis to extract meaningful information from material presented on the Internet. This article describes the development of a text editor that uses morphological and syntactic analyzers to process website content. The main purpose of the system is to automatically mark and improve the text content of web pages by integrating semantic information obtained based on text structure and meaning analysis. The system uses preprocessing technology and deep NLP models (such as BERT), combined with a self-built Kazakh tree library (ConLL-U format) to restore the query's lemmatization. Optimize the matching of queries and documents through TF-IDF, Word2Vec and query expansion technology. Experiments were conducted on an autonomously collected Kazakh news dataset. Experimental results show that morphological analysis significantly improves retrieval accuracy during the text normalization process, while the deep learning model further enhances the ranking effect based on semantic correlation. This research provides new technical means for content optimization and information retrieval in low-resource languages, and helps improve the performance of search engines in multi-language environments.