  We are presenting work on recognising acronyms of the form Long-Form
(Short-Form) such as "International Monetary Fund (IMF)" in millions of news
articles in twenty-two languages, as part of our more general effort to
recognise entities and their variants in news text and to use them for the
automatic analysis of the news, including the linking of related news across
languages. We show how the acronym recognition patterns, initially developed
for medical terms, needed to be adapted to the more general news domain and we
present evaluation results. We describe our effort to automatically merge the
numerous long-form variants referring to the same short-form, while keeping
non-related long-forms separate. Finally, we provide extensive statistics on
the frequency and the distribution of short-form/long-form pairs across
languages.
