Bilingual Lexicon Induction (BLI) is a valuable tool in machine translation and cross-lingual transfer learning, but it remains challenging for agglutinative and low-resource languages. In this work, we investigate the use of weighted sub-word embeddings in BLI for agglutinative languages. We further evaluate a graph-matching and Procrustes-based BLI approach on two Bantu languages, assessing its effectiveness in a previously underexplored language family. Our results for Swahili with an average P@1 score of 51.84% for a 3000 word dictionary demonstrate the success of the approach for Bantu languages. Weighted sub-word embeddings perform competitively on Swahili and outperform word embeddings in our experiments with Zulu.

This paper introduces Fundus, a user-friendly news scraper that enables users to obtain millions of high-quality news articles with just a few lines of code. Unlike existing news scrapers, we use manually crafted, bespoke content extractors that are specifically tailored to the formatting guidelines of each supported online newspaper. This allows us to optimize our scraping for quality such that retrieved news articles are textually complete and without HTML artifacts. Further, our framework combines both crawling (retrieving HTML from the web or large web archives) and content extraction into a single pipeline. By providing a unified interface for a predefined collection of newspapers, we aim to make Fundus broadly usable even for non-technical users. This paper gives an overview of the framework, discusses our design choices, and presents a comparative evaluation against other popular news scrapers. Our evaluation shows that Fundus yields significantly higher quality extractions (complete and artifact-free news articles) than prior work.The framework is available on GitHub under https://github.com/flairNLP/fundus and can be simply installed using pip.