The Handbook of Natural Language Processing, Second Edition presents practical tools and techniques for implementing natural language processing in computer systems. Along with removing outdated material, this edition updates every chapter and expands the content to include emerging areas, such as sentiment analysis.New to the Second EditionGreater

This volume offers the reader a unique possibility to obtain a concise introduction to dependency linguistics and to learn about the current state of the art in the field. It unites the revised and extended versions of the linguistically-oriented papers to the First International Conference on Dependency Linguistics held in Barcelona. The contributions range from the discussion of definitional challenges of dependency at different levels of the linguistic model, its role beyond the classical grammatical description, and its annotation in dependency treebanks to concrete analyses of various cross-linguistic phenomena of syntax in its interplay with phonetics, morphology, and semantics, including phenomena for which classical simple phrase-structure based models have proven to be unsatisfactory. The volume will be thus of interest to both experts and newcomers to the field of dependency linguistics and its computational applications.

Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well. Table of Contents: Introduction / MapReduce Basics / MapReduce Algorithm Design / Inverted Indexing for Text Retrieval / Graph Algorithms / EM Algorithms for Text Processing / Closing Remarks

The genesis of this volume was the participation of the editors in an ACMlSIGIR (Association for Computing Machinery/Special Interest Group on Information Retrieval) workshop entitled "Beyond Word Relations" (Hetzler, 1997). This workshop examined a number of relationship types with significance for information retrieval beyond the conventional topic-matching relationship. From this shared participation came the idea for an edited volume on relationships, with chapters to be solicited from researchers and practitioners throughout the world. Ultimately, one volume became two volumes. The first volume, Relationships in the Organization of Knowledge (Bean & Green, 200 I), examines the role of relationships in knowledge organization theory and practice, with emphasis given to thesaural relationships and integration across systems, languages, cultures, and disciplines. This second volume examines relationships in a broader array of contexts. The two volumes should be seen as companions, each informing the other. As with the companion volume, we are especially grateful to the authors who willingly accepted challenges of space and time to produce chapters that summarize extensive bodies of research. The value of the volume clearly resides in the quality of the individual chapters. In naming this volume The Semantics of Relationships: An Interdisciplinary Perspective, we wanted to highlight the fact that relationships are not just empty connectives. Relationships constitute important conceptual units and make significant contributions to meaning.

The majority of natural language processing (NLP) is English language processing, and while there is good language technology support for (standard varieties of) English, support for Albanian, Burmese, or Cebuano--and most other languages--remains limited. Being able to bridge this digital divide is important for scientific and democratic reasons but also represents an enormous growth potential. A key challenge for this to happen is learning to align basic meaning-bearing units of different languages. In this book, the authors survey and discuss recent and historical work on supervised and unsupervised learning of such alignments. Specifically, the book focuses on so-called cross-lingual word embeddings. The survey is intended to be systematic, using consistent notation and putting the available methods on comparable form, making it easy to compare wildly different approaches. In so doing, the authors establish previously unreported relations between these methods and are able to present a fast-growing literature in a very compact way. Furthermore, the authors discuss how best to evaluate cross-lingual word embedding methods and survey the resources available for students and researchers interested in this topic.

No detailed description available for "Markov Models and Linguistic Theory".

This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.