EMM is a software toolset that monitors a daily average of 25,000 news articles in currently 30 languages, deriving from 800 different international news sources. Due to the highly multilingual work environment in the European Commission – an organisation with twenty official languages – multilinguality of tools and the cross-lingual aspect are of prime importance.ģ Our analysis is applied to the output of the Europe Media Monitor system EMM (Best et al., 2002). Previous work focused on answering the questions What (Pouliquen et al. This paper focuses on the occurrence of proper names in news, i.e. This seems plausible as, according to Gey (2000), 30% of content-bearing words in news are proper names.Ģ In news analysis it is important to know What is the subject, Who is being talked about, Where and When things happened, and How it was reported.
Crestan & de Loupy (2004) showed that Named Entity extraction and visualisation help users to browse large document collections more quickly and efficiently. Software tools that automatically pre-select the news articles of interest and that pre-process the chosen text collection simplify the daily repetitive task of media monitoring. Introductionġ Many large organisations continuously monitor the media, and especially the news, to stay informed about events of interest, and to find out what the media say about certain persons, organisations, or subjects. We thank Tomaž Erjavec for helping us with the Slovene language, and Helen Salak for providing us with knowledge about Farsi. We also want to thank Carlo Ferigato who introduced us to various fuzzy matching techniques. We thank the whole team of the Web Technology sector at the JRC for providing us with the valuable news data to test the tools, as well as for their technical support.
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