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"This book present a comprehensive description of the technologies involved in designing and developing systems for Multilingual Information Retrieval (MLIR). They provide readers with broad coverage of the various issues involved in creating systems to make accessible digitally stored materials regardless of the language(s) they are written in. Details on Cross-Language Information Retrieval (CLIR) are also covered that help readers to understand how to develop retrieval systems that cross language boundaries. Their work is divided into six chapters and accompanies the reader step-by-step through the various stages involved in building, using and evaluating MLIR systems. The book concludes with some examples of recent applications that utilise MLIR technologies. Some of the techniques described have recently started to appear in commercial search systems, while others have the potential to be part of future incarnations."

"Machine Learning (ML) algorithms have opened up new possibilitiesfor the acquisition and processing of documents in InformationRetrieval (IR) systems. Indeed, it is now possible to automate severallabor-intensive tasks related to documents such as categorization andentity extraction. Consequently, the application of machine learning techniquesfor various large-scale IR tasks has gathered significant researchinterest in both the ML and IR communities. This tutorial provides areference summary of our research in applying machine learning techniquesto diverse tasks in Digital Libraries (DL). Digital library portalsare specialized IR systems that work on collections of documentsrelated to particular domains. We focus on open-access, scientific digitallibraries such as CiteSeerx, which involve several crawling, ranking,content analysis, and metadata extraction tasks. We elaborate on thechallenges involved in these tasks and highlight how machine learningmethods can successfully address these challenges."