News Across Languages - Cross-Lingual Document Similarity and Event Tracking

TL;DR

This paper presents a scalable cross-lingual document similarity measure based on Wikipedia, enabling effective event tracking across multiple languages in a global news stream.

Contribution

It introduces a novel approach to compare and link multilingual news articles for event tracking, even with limited training data in some languages.

Findings

Effective cross-lingual similarity measure based on Wikipedia

Scalable methods for linking articles across languages

Robust evaluation demonstrating system performance

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.