  In today's world, we follow news which is distributed globally. Significant
events are reported by different sources and in different languages. In this
work, we address the problem of tracking of events in a large multilingual
stream. Within a recently developed system Event Registry we examine two
aspects of this problem: how to compare articles in different languages and how
to link collections of articles in different languages which refer to the same
event. Taking a multilingual stream and clusters of articles from each
language, we compare different cross-lingual document similarity measures based
on Wikipedia. This allows us to compute the similarity of any two articles
regardless of language. Building on previous work, we show there are methods
which scale well and can compute a meaningful similarity between articles from
languages with little or no direct overlap in the training data. Using this
capability, we then propose an approach to link clusters of articles across
languages which represent the same event. We provide an extensive evaluation of
the system as a whole, as well as an evaluation of the quality and robustness
of the similarity measure and the linking algorithm.
