Extraction of structured information by machine learning using community information

Abstract

In this paper, we describe our approach for information extraction from documents, which is based on supervised machine learning and collective intelligence approach. This approach is aimed at redeeming each method, because each method has merits and demerits. It provides various ways for users to input data to improve information extraction. Users can add not only supervised data but also a rule to extract values for a set of attributes. Various ways to input data allows many users to add a lot of data for quality improvement and machine learning can reduce noise of data input by users. We implemented it in event-information extraction system, and the experimental result shows effectiveness in correctness and convenience.