|Title||A High Accuracy Method for Semi-supervised Information Extraction|
|Publication Type||Conference Paper|
|Year of Publication||2007|
|Authors||Tratz S, Sanfilippo A|
|Conference Name||NAACL HLT 2007|
|Publisher||Association for Computational Linguistics|
Customization to specific domains of discourse 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. Semisupervised 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 semisupervised IE approach, without increasing resource requirements.