News Across Languages - Cross-Lingual Document Similarity and Event Tracking
This addresses the challenge of global news monitoring for media analysts and researchers by enabling efficient cross-lingual event tracking, though it is incremental as it builds on existing systems and methods.
The paper tackled the problem of tracking events in multilingual news streams by developing cross-lingual document similarity measures based on Wikipedia to compare articles in different languages and linking clusters of articles that refer to the same event, with an extensive evaluation showing scalable methods that work even for languages with little training data overlap.
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.