A major part of natural language processing now depends on the use of text data to build linguistic analyzers. We consider statistical, computational approaches to modeling linguistic structure. We seek to unify across many approaches and many kinds of linguistic structures. Assuming a basic understanding of natural language processing and/or machine learning, we seek to bridge the gap between the two fields. Approaches to decoding (i.e., carrying out linguistic structure prediction) and supervised and unsupervised learning of models that predict discrete structures as outputs are the focus. We also survey natural language processing problems to which these methods are being applied, and we address related topics in probabilistic inference, optimization, and experimental methodology. Table of Contents: Representations and Linguistic Data / Decoding: Making Predictions / Learning Structure from Annotated Data / Learning Structure from Incomplete Data / Beyond Decoding: Inference

A major part of natural language processing now depends on the use of text data to build linguistic analyzers. We consider statistical, computational approaches to modeling linguistic structure. We seek to unify across many approaches and many kinds of linguistic structures. Assuming a basic understanding of natural language processing and/or machine learning, we seek to bridge the gap between the two fields. Approaches to decoding (i.e., carrying out linguistic structure prediction) and supervised and unsupervised learning of models that predict discrete structures as outputs are the focus. We also survey natural language processing problems to which these methods are being applied, and we address related topics in probabilistic inference, optimization, and experimental methodology. Table of Contents: Representations and Linguistic Data / Decoding: Making Predictions / Learning Structure from Annotated Data / Learning Structure from Incomplete Data / Beyond Decoding: Inference

There is ample evidence that language users, including second-language (L2) users, can predict upcoming information during listening and reading. Yet it is still unclear when, how, and why language users engage in prediction, and what the relation is between prediction and learning. This volume presents a collection of current research, insights, and directions regarding the role of prediction in L2 processing and learning. The contributions in this volume specifically address how different (L1-based) theoretical models of prediction apply to or may be expanded to account for L2 processing, report new insights on factors (linguistic, cognitive, social) that modulate L2 users’ engagement in prediction, and discuss the functions that prediction may or may not serve in L2 processing and learning. Taken together, this volume illustrates various fruitful approaches to investigating and accounting for differences in predictive processing within and across individuals, as well as across populations.

Thomas Berg challenges context-free theories of linguistics; he is concerned with the way the term 'explanation' is typically used in the discipline. He argues that real explanations cannot emerge from a view which asserts the autonomy of language, but only from an approach which seeks to establish a connection between language and the contexts in which it is embedded. The author examines the psychological context in detail. He uses an interactiveactivation model of language processing to derive predictions about synchronic linguistic patterns, the course of linguistic change, and the structure of poetic rhymes. The majority of these predictions are borne out, leading the author to conclude that the structure of language is shaped by the properties of the mechanism which puts it to use, and that psycholinguistics thus qualifies as one likely approach from which to derive an explanation of linguistic structure.

An overview of recent work in the field of structured prediction, the building of predictive machine learning models for interrelated and dependent outputs. The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. They can carry out such tasks as predicting a natural language sentence, or segmenting an image into meaningful components. These models are expressive and powerful, but exact computation is often intractable. A broad research effort in recent years has aimed at designing structured prediction models and approximate inference and learning procedures that are computationally efficient. This volume offers an overview of this recent research in order to make the work accessible to a broader research community. The chapters, by leading researchers in the field, cover a range of topics, including research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning. Contributors Jonas Behr, Yutian Chen, Fernando De La Torre, Justin Domke, Peter V. Gehler, Andrew E. Gelfand, Sébastien Giguère, Amir Globerson, Fred A. Hamprecht, Minh Hoai, Tommi Jaakkola, Jeremy Jancsary, Joseph Keshet, Marius Kloft, Vladimir Kolmogorov, Christoph H. Lampert, François Laviolette, Xinghua Lou, Mario Marchand, André F. T. Martins, Ofer Meshi, Sebastian Nowozin, George Papandreou, Daniel Průša, Gunnar Rätsch, Amélie Rolland, Bogdan Savchynskyy, Stefan Schmidt, Thomas Schoenemann, Gabriele Schweikert, Ben Taskar, Sinisa Todorovic, Max Welling, David Weiss, Thomáš Werner, Alan Yuille, Stanislav Živný

University of Massachusetts Occasional Papers in Linguistics, Vol. 38: Processing Linguistic Structure

"The workshop that originated this book was entitled "Understanding language : forty years down the garden path". It took place in July 2010." --Acknowledgements p. [xii].