Unsupervised information extraction approach using graph mutual reinforcement

Abstract

Information Extraction (IE) is the task of extracting knowledge from unstructured text. We present a novel unsupervised approach for information extraction based on graph mutual reinforcement. The proposed approach does not require any seed patterns or examples. Instead, it depends on redundancy in large data sets and graph based mutual reinforcement to induce generalized "extraction patterns". The proposed approach has been used to acquire extraction patterns for the ACE (Automatic Content Extraction) Relation Detection and Characterization (RDC) task. ACE RDC is considered a hard task in information extraction due to the absence of large amounts of training data and inconsistencies in the available data. The proposed approach achieves superior performance which could be compared to supervised techniques with reasonable training data. © 2006 Association for Computational Linguistics.