Paper Info
Title
News Across Languages - Cross-Lingual Document Similarity and Event Tracking.
Abstract
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.
Year
DOI
Venue
2016
10.1613/jair.4780
JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
Field
DocType
Volume
Training set,Cross lingual,Similarity measure,Robustness (computer science),Natural language processing,Artificial intelligence,Document similarity,Event tracking,Machine learning,Mathematics
Journal
55
Issue
ISSN
Citations 
1
1076-9757
6
PageRank 
References 
Authors
0.64
12
6
Name
Order
Citations
PageRank
Jan Rupnik1225.04
Andrej Muhic2142.90
Gregor Leban323720.51
Primoz Skraba426022.26
blaž fortuna523220.61
Marko Grobelnik61032126.90