Nowadays, many influential facts are reported multiple times by different sources and in different languages. This paper presents the results of an experiment on deploying cross-lingual information fusion techniques for refining the results of a large-scale multilingual news event extraction system. An evaluation on a test corpus consisting of 618 event descriptions which refer to 523 real-world events revealed that the description of circa 10% of the events extracted by the mono-lingual systems could be refined. In particular, an overall gain of 6,4% and 4,8% in recall and precision against the best monolingual system could be obtained respectively.
Another related work is that of REF , who use cross-lingual information to refine the results of event extraction.
Exploring the Usefulness of Cross-lingual Information Fusion for Refining Real-time News Event Extraction: A Preliminary Study
RANLP
Computer Science