为东帝汶语构建首个信息检索基础，包含停用词、词干提取器和测试集。

Searching for information on the internet and digital platforms requires effective retrieval solutions. However, such solutions are not yet available for Tetun, making it difficult to find relevant documents for search queries in this language. To address this gap, we investigate Tetun text retrieval with a focus on the ad-hoc retrieval task. The study begins with the development of essential language resources -- including a list of stopwords, a stemmer, and a test collection -- that serve as a foundation for Tetun text retrieval. Various strategies are evaluated using document titles and content. The results show that retrieving document titles, after removing hyphens and apostrophes but without applying stemming, improves performance compared to the baseline. Efficiency increases by 31.37%, while effectiveness achieves an average relative gains of +9.40% in MAP@10 and +30.35% in NDCG@10 with DFR BM25. Beyond the top-10 cutoff point, Hiemstra LM demonstrates strong performance across multiple retrieval strategies and evaluation metrics. The contributions of this work include the development of Labadain-Stopwords (a list of 160 Tetun stopwords), Labadain-Stemmer (a Tetun stemmer with three variants), and Labadain-Avaliadór (a Tetun test collection comprising 59 topics, 33,550 documents, and 5,900 qrels). These resources are publicly available to support future research in Tetun information retrieval.