Interactive Information Extraction with Constrained Conditional Random Fields

Information Extraction methods can be used to automatically "fill-in" database forms from unstructured data such as Web documents or email. State-of-the-art methods have achieved low error rates but invariably make a number of errors. The goal of an interactive information extraction system is to assist the user in filling in database fields while giving the user confidence in the integrity of the data. The user is presented with an interactive interface that allows both the rapid verification of automatic field assignments and the correction of errors. In cases where there are multiple errors, our system takes into account user corrections, and immediately propagates these constraints such that other fields are often corrected automatically. Linear-chain conditional random fields (CRFs) have been shown to perform well for information extraction and other language modelling tasks due to their ability to capture arbitrary, overlapping features of the input in a Markov model. ...

• Type: Conference
• Year: 2004
• Where: AAAI
• Authors: Trausti T. Kristjansson, Aron Culotta, Paul A. Viola, Andrew McCallum
• Keywords: AAAI 2004, Field Assignments, Information Extraction, Intelligent Agents, User