(untitled) («CONTEMPORARY TECHNOLOGIES OF COMPUTATIONAL LINGUISTICS», 2024)
Today, text processing and analysis tasks are crucial in developing the NLP field. Most of the tasks in this field, like text generation, sentiment analysis, and machine translation, require many language resources. While multi-resource languages, such as English, German, French, Chinese, and others, benefit from large, diverse corpora that facilitate the development of robust language models, low-resource languages face significant challenges related to data scarcity. This paper focuses on the lack of resources for the Kazakh language, which is one of the languages in the Turkic group, for high-quality model training in various NLP tasks. In order to increase the size of the available corpora, the research proposes the approach of parsing the Adilet legislative website, which includes a very large collection of well-structured and error- free texts. The dataset was gathered in two phases. In the first step, the links to the Adilet website were collected using the Content Downloader program. Then, the parser based on the Selenium WebDriver was utilized to extract data and form a dataset with the title, the date of the law article, its text, and a URL link. The total corpora included 9575 texts.