In the last few years the Internet has become a prominent vehicle for communication with the side effect that digital media also has become more relevant for criminal and terrorist activities. This necessitates the surveillance of these activities on the Internet. A simple way to monitor content is the spotting of suspicious words and phrases in texts. Yet one of the problems with simply looking for words is the ambiguity of words, whose meaning often depends on context. Information extraction aims at recovering the meaning of words and phrases from the neighboring words. We give an overview of term and relation extraction methods based on pattern matching and trainable statistical methods and report on experiments of semi-supervised training of such methods.
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