Customization to specific domains of dis- course and/or user requirements is one of the greatest challenges for today�s Infor- mation Extraction (IE) systems. While demonstrably effective, both rule-based and supervised machine learning ap- proaches to IE customization pose too high a burden on the user. Semi- supervised learning approaches may in principle offer a more resource effective solution but are still insufficiently accurate to grant realistic application. We demonstrate that this limitation can be overcome by integrating fully-supervised learning techniques within a semi- supervised IE approach, without increasing resource requirements.

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