A High Accuracy Method for Semi-Supervised Information Extraction

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.

• Type: Conference
• Year: 2007
• Where: NAACL
• Authors: Stephen Tratz, Antonio Sanfilippo
• Keywords: Computational Linguistics, Discourse And/or User, Machine Learning Approaches, NAACL 2007, Semisupervised Ie Approach