A High Accuracy Method for Semi-supervised Information Extraction

Customization to specific domains of dis-course and/or user requirements is one of the greatest challenges for today’s Information Extraction (IE) systems. While demonstrably effective, both rule-based and supervised machine learning approaches 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