Extracting Structured Subject Information from Digital Document Archives

Abstract

Information extraction (IE) techniques are capable of decoding targeted subject information in documents, and reducing text data into a set of structured core information. The implication for digital libraries is that IE potentially serves as an enabling tool to extend the value of digital document archives. We present an approach, called sandwich extraction pattern, to address the closely coupled template relation tasks. The approach provides interactive capabilities for task specification, domain knowledge acquisition, and output evaluation. This allows users (e.g. librarians) to have direct control on the design of value-added content products and the performance of IE tools. We conducted empirical validation by implementing an IE system, called SEP, and field testing it in a practical document archive. Encouraged by successful test runs, NCCU library has formally initiated a project to develop a value-added content product of government personnel gazettes, including document images, electronic texts, and personnel changes database.