  Multilingual text processing is useful because the information content found
in different languages is complementary, both regarding facts and opinions.
While Information Extraction and other text mining software can, in principle,
be developed for many languages, most text analysis tools have only been
applied to small sets of languages because the development effort per language
is large. Self-training tools obviously alleviate the problem, but even the
effort of providing training data and of manually tuning the results is usually
considerable. In this paper, we gather insights by various multilingual system
developers on how to minimise the effort of developing natural language
processing applications for many languages. We also explain the main guidelines
underlying our own effort to develop complex text mining software for tens of
languages. While these guidelines - most of all: extreme simplicity - can be
very restrictive and limiting, we believe to have shown the feasibility of the
approach through the development of the Europe Media Monitor (EMM) family of
applications (http://emm.newsbrief.eu/overview.html). EMM is a set of complex
media monitoring tools that process and analyse up to 100,000 online news
articles per day in between twenty and fifty languages. We will also touch upon
the kind of language resources that would make it easier for all to develop
highly multilingual text mining applications. We will argue that - to achieve
this - the most needed resources would be freely available, simple, parallel
and uniform multilingual dictionaries, corpora and software tools.
