Navigating multilingual news collections using automatically extracted information

TL;DR

This paper introduces a multilingual news analysis tool that automatically clusters articles, extracts key entities, links related information, and learns relationships over time to facilitate efficient navigation of large news collections.

Contribution

It presents a comprehensive tool set for multilingual news analysis that integrates clustering, entity extraction, linking, and relationship learning, enabling effective exploration of large collections.

Findings

Successfully clusters multilingual news articles

Automatically extracts and links entities across languages

Learns relationships between entities over time

Abstract

We are presenting a text analysis tool set that allows analysts in various fields to sieve through large collections of multilingual news items quickly and to find information that is of relevance to them. For a given document collection, the tool set automatically clusters the texts into groups of similar articles, extracts names of places, people and organisations, lists the user-defined specialist terms found, links clusters and entities, and generates hyperlinks. Through its daily news analysis operating on thousands of articles per day, the tool also learns relationships between people and other entities. The fully functional prototype system allows users to explore and navigate multilingual document collections across languages and time.