This book is a reflection of about twelve years of work on memory-based language processing. It reflects on the central topic from three perspectives. First, it describes the influences from linguistics, artificial intelligence, and psycholinguistics on the foundations of memory-based models of language processing. Second, it highlights applications of memory-based learning to processing tasks in phonology and morphology, and in shallow parsing. Third, it ventures into answering the question why memory-based learning fills a unique role in the larger field of machine learning of natural language – because it is the only algorithm that does not abstract away from its training examples. In addition, we provide tutorial information on the use of TIMBL, a software package for memory-based learning, and an associated suite of software tools for memory-based language processing.

For us, the direct inspiration for starting to experiment with extensions of the k-nearest neighbor classifier to language processing problems was the successful application of the approach by Stanfill and Waltz to grapheme-to-phoneme conversion in the eighties. During the past decade we have been fortunate to have expanded our work with a great team of fellow researchers and students on memory-based language processing in two locations: the ILK (Induction of Linguistic Knowledge) research group at Tilburg University, and CNTS (Center for Dutch Language and Speech) at the University of Antwerp. Our own first implementations of memory-based learning were soon superseded by well-coded software systems by Peter Berck, Jakub Zavrel, Bertjan Busser, and Ko van der Sloot.