On Cross-Script Information Retrieval

We address the problem of cross-script retrieval in the context of a microblog system such as Twitter. Specifically, we explore methods for using native Arabic script queries to retrieve Arabic tweets written in a Roman script known as Arabizi. For example, a query for “كتاب” would not match “kitab” even though an Arabic reader would see them as the same word. Moreover, because of the lack of Arabic script, automatic language identification methods fail to recognize the Arabizi text as Arabic and label it as English, Polish, or the like. We propose a cross-script retrieval system using automatic rule-based mapping and statistical selection of transliteration keywords. We show that our system can achieve effective cross-script retrieval with minimal knowledge of the target language and without the need to rely on external translation or transliteration tools or lexica. With minimal human annotation, our technique can be applied to other languages such as Hindi and Greek, which are commonly converted to a Roman character set similarly.