"Machine Learning (ML) algorithms have opened up new possibilities
for the acquisition and processing of documents in Information
Retrieval (IR) systems. Indeed, it is now possible to automate several
labor-intensive tasks related to documents such as categorization and
entity extraction. Consequently, the application of machine learning techniques
for various large-scale IR tasks has gathered significant research
interest in both the ML and IR communities. This tutorial provides a
reference summary of our research in applying machine learning techniques
to diverse tasks in Digital Libraries (DL). Digital library portals
are specialized IR systems that work on collections of documents
related to particular domains. We focus on open-access, scientific digital
libraries such as CiteSeerx, which involve several crawling, ranking,
content analysis, and metadata extraction tasks. We elaborate on the
challenges involved in these tasks and highlight how machine learning
methods can successfully address these challenges."
