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Memory-Based Language Processing
Memory-based language processing--a machine learning and problem solving method for language technology--is based on the idea that the direct re-use of examples using analogical reasoning is more suited for solving language processing problems than the application of rules extracted from those examples. This book discusses the theory and practice of memory-based language processing, showing its comparative strengths over alternative methods of language modelling. Language is complex, with few generalizations, many sub-regularities and exceptions, and the advantage of memory-based language processing is that it does not abstract away from this valuable low-frequency information.
Analogical Modeling
Analogical Modeling (AM) is an exemplar-based general theory of description that uses both neighbors and non-neighbors (under certain well-defined conditions of homogeneity) to predict language behavior. This book provides a basic introduction to AM, compares the theory with nearest-neighbor approaches, and discusses the most recent advances in the theory, including psycholinguistic evidence, applications to specific languages, the problem of categorization, and how AM relates to alternative approaches of language description (such as instance families, neural nets, connectionism, and optimality theory). The book closes with a thorough examination of the problem of the exponential explosion, an inherent difficulty in AM (and in fact all theories of language description). Quantum computing (based on quantum mechanics with its inherent simultaneity and reversibility) provides a precise and natural solution to the exponential explosion in AM. Finally, an extensive appendix provides three tutorials for running the AM computer program (available online).
Machine Learning: ECML 2003
This book constitutes the refereed proceedings of the 14th European Conference on Machine Learning, ECML 2003, held in Cavtat-Dubrovnik, Croatia in September 2003 in conjunction with PKDD 2003. The 40 revised full papers presented together with 4 invited contributions were carefully reviewed and, together with another 40 ones for PKDD 2003, selected from a total of 332 submissions. The papers address all current issues in machine learning including support vector machine, inductive inference, feature selection algorithms, reinforcement learning, preference learning, probabilistic grammatical inference, decision tree learning, clustering, classification, agent learning, Markov networks, boosting, statistical parsing, Bayesian learning, supervised learning, and multi-instance learning.
Deterministic and Statistical Methods in Machine Learning
This book consitutes the refereed proceedings of the First International Workshop on Machine Learning held in Sheffield, UK, in September 2004. The 19 revised full papers presented were carefully reviewed and selected for inclusion in the book. They address all current issues in the rapidly maturing field of machine learning that aims to provide practical methods for data discovery, categorisation and modelling. The particular focus of the workshop was advanced research methods in machine learning and statistical signal processing.
Computational Linguistics in the Netherlands 2000
This volume provides a selection of the papers which were presented at the eleventh conference on Computational Linguistics in the Netherlands (Tilburg, 2000). It gives an accurate and up-to-date picture of the lively scene of computational linguistics in the Netherlands and Flanders. The volume covers the whole range from theoretical to applied research and development, and is hence of interest to both academia and industry. The target audience consists of students and scholars of computational linguistics, and speech and language processing (Linguistics, Computer Science, Electrical Engineering).
European Language Equality
This open access book presents a comprehensive collection of the European Language Equality (ELE) project’s results, its strategic agenda and roadmap with key recommendations to the European Union on how to achieve digital language equality in Europe by 2030. The fabric of the EU linguistic landscape comprises 24 official languages and over 60 regional and minority languages. However, language barriers still hamper communication and the free flow of information. Multilingualism is a key cultural cornerstone of Europe, signifying what it means to be and to feel European. Various studies and resolutions have found a striking imbalance in the support of Europe’s languages through technologi...
CLARIN in the Low Countries
This book describes the results of activities undertaken to construct the CLARIN research infrastructure in the Low Countries, i.e., in the Netherlands and in Flanders (the Dutch-speaking part of Belgium). CLARIN is a European research infrastructure for humanities and social science researchers that work with natural language data. This book introduces the CLARIN infrastructure, describes various aspects of the technical implementation of the infrastructure, and introduces data, applications and software services created in the Low Countries for a wide variety of humanities disciplines. These enable researchers to accelerate their research activities and to base their conclusions on a much ...
Memory-based Parsing
Memory-Based Learning (MBL), one of the most influential machine learning paradigms, has been applied with great success to a variety of NLP tasks. This monograph describes the application of MBL to robust parsing. Robust parsing using MBL can provide added functionality for key NLP applications, such as Information Retrieval, Information Extraction, and Question Answering, by facilitating more complex syntactic analysis than is currently available. The text presupposes no prior knowledge of MBL. It provides a comprehensive introduction to the framework and goes on to describe and compare applications of MBL to parsing. Since parsing is not easily characterizable as a classification task, adaptations of standard MBL are necessary. These adaptations can either take the form of a cascade of local classifiers or of a holistic approach for selecting a complete tree.The text provides excellent course material on MBL. It is equally relevant for any researcher concerned with symbolic machine learning, Information Retrieval, Information Extraction, and Question Answering.
