Paper: A High Accuracy Method for Semi-Supervised Information Extraction

ACL ID N07-2043
Title A High Accuracy Method for Semi-Supervised Information Extraction
Venue Human Language Technologies
Session Short Paper
Year 2007
Authors

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.