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The web and the social web play an increasingly important role as an information source for Members of Parliament and their assistants, journalists, political analysts and researchers. It provides important and crucial background information, like reactions to political events and comments made by the general public. The case study presented in this paper is driven by two European parliaments (the Greek and the Austrian parliament) and targets an effective exploration of political web archives. In this paper, we describe semantic technologies deployed to ease the exploration of the archived web and social web content and present evaluation results.
High impact events, political changes and new technologies are reflected in our language and lead to constant evolution of terms, expressions and names. Not knowing about names used in the past for referring to a named entity can severely decrease the performance of many computational linguistic algorithms. We propose NEER, an unsupervised method for named entity evolution recognition independent of external knowledge sources. We find time periods with high likelihood of evolution. By analyzing only these time periods using a sliding window co-occurrence method we capture evolving terms in the same context. We thus avoid comparing terms from widely different periods in time and overcome a severe limitation of existing methods for named entity evolution, as shown by the high recall of 90% on the New York Times corpus. We compare several relatedness measures for filtering to improve precision. Furthermore, using machine learning with minimal supervision improves precision to 94%.