Search papers, labs, and topics across Lattice.
This paper introduces a pipeline for creating high-quality textual corpora from Wikimedia dumps for seven South Slavic languages, encompassing Wikipedia, Wikisource, Wikibooks, Wikinews, and Wikiquote. The pipeline extracts and cleans text from raw dumps, handling wiki markup to isolate usable natural language. A novel n-gram-based filtering strategy identifies and removes low-quality articles characterized by textual redundancy, resulting in linguistically rich datasets suitable for language model training.
A simple n-gram filter can effectively purge machine-generated content from Wikipedia dumps, yielding higher-quality training corpora.
This paper presents a methodology for transforming raw Wikimedia dumps into quality textual corpora for seven South Slavic languages. The work is divided into two major phases. The first involves extracting and cleaning text from raw dumps of Wikipedia, Wikisource, Wikibooks, Wikinews, and Wikiquote, where available. This step requires careful handling of raw wiki markup to isolate, first of all, textual articles, and then usable natural language text within them. The second phase addresses the challenge of suspicious or low-quality articles, which are often generated from databases or structured knowledge bases. These articles are characterised by repetitive patterns, generic phrasing, and minimal to no original content. To mitigate their impact, a n-gram-based filtering strategy was employed to detect high levels of textual redundancy between articles and then remove such articles from the corpora entirely. The resulting datasets aim to provide linguistically rich texts suitable for training language models or conducting comparative research across South Slavic languages. By combining systematic extraction with quality control, this work contributes to the creation of reliable, high-information corpora that reflect authentic language use and cultural context. While focused on the South Slavic case in the paper, the approach is mostly language-agnostic and can be generalised to other languages and language families.
