We are presenting a method to recognise geographical references in free text. Our tool must work on various languages with a minimum of language-dependent resources, except a gazetteer. The main difficulty is to disambiguate these place names by distinguishing places from persons and by selecting the most likely place out of a list of homographic place names world-wide. The system uses a number of language-independent clues and heuristics to disambiguate place name homographs. The final aim is to index texts with the countries and cities they mention and to automatically visualise this information on geographical maps using various tools.

An automatic news tracking and analysis system which records world events over long time periods is described. It allows to track country specific news, the activities of individual persons and groups, to derive trends, and to provide data for further analysis and research. The data source is the Europe Media Monitor (EMM) which monitors news from around the world in real time via the Internet and from various News Agencies. EMM’s main purpose is to provide rapid feedback of press coverage and breaking news for European Policy Makers. Increasingly, however it is being used for security applications and for foreign policy monitoring. This paper describes how language technologies and clustering techniques have been applied to the 30,000 daily news reports to derive the top stories in each of 13 languages, to locate events geospatially, and to extract and record entities involved. Related stories have been linked across time and across languages, allowing for national comparisons and to derive name variants. Results and future plans are described.

We present a tool that, from automatically recognised names, tries to infer inter-person relations in order to present associated people on maps. Based on an in-house Named Entity Recognition tool, applied on clusters of an average of 15,000 news articles per day, in 15 different languages, we build a knowledge base that allows extracting statistical co-occurrences of persons and visualising them on a per-person page or in various graphs.