Learning structure from sequences, with applications in a digital library

Abstract

The services that digital libraries provide to users can be greatly enhanced by automatically gleaning certain kinds of information from the full text of the documents they contain. This paper reviews some recent work that applies novel techniques of machine learning (broadly interpreted) to extract information from plain text, and puts it in the context of digital library applications. We describe three areas: hierarchical phrase browsing, including efficient methods for inferring a phrase hierarchy from a large corpus of text; text mining using adaptive compression techniques, giving a new approach to generic entity extraction, word segmentation, and acronym extraction; and keyphrase extraction.