Smart Bilingual Focused Crawling of Parallel Documents
<|reference_start|>Smart Bilingual Focused Crawling of Parallel Documents: Crawling parallel texts $\unicode{x2014}$texts that are mutual translations$\unicode{x2014}$ from the Internet is usually done following a brute-force approach: documents are massively downloaded in an unguided process, and only a fraction of them end up leading to actual parallel content. In this work we propose a smart crawling method that guides the crawl towards finding parallel content more rapidly. Our approach builds on two different models: one that infers the language of a document from its URL, and another that infers whether a pair of URLs link to parallel documents. We evaluate both models in isolation and their integration into a crawling tool. The results demonstrate the individual effectiveness of both models and highlight that their combination enables the early discovery of parallel content during crawling, leading to a reduction in the amount of downloaded documents deemed useless, and yielding a greater quantity of parallel documents compared to conventional crawling approaches.<|reference_end|>
@article{garcía-romero2024smart,
    title={Smart Bilingual Focused Crawling of Parallel Documents},
    author={Cristian Garc'ia-Romero, Miquel Espl`a-Gomis, Felipe
  S'anchez-Mart'inez},
    journal={arXiv preprint arXiv:2405.14779},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14779},
    primaryClass={cs.CL cs.LG}
}