Semantic Processing For Finite Domains

A primary problem in the area of natural language processing has been semantic analysis. This book looks at the semantics of natural languages in context. It presents an approach to the computational processing of English text that combines current theories of knowledge representation and reasoning in Artificial Intelligence with the latest linguistic views of lexical semantics. The book will interest postgraduates and researchers in computational linguistics as well as industrial research groups specializing in natural language processing.

This book and CD-ROM cover the breadth of contemporary finite state language modeling, from mathematical foundations to developing and debugging specific grammars.

This book is aimed at providing an overview of several aspects of semantic role labeling. Chapter 1 begins with linguistic background on the definition of semantic roles and the controversies surrounding them. Chapter 2 describes how the theories have led to structured lexicons such as FrameNet, VerbNet and the PropBank Frame Files that in turn provide the basis for large scale semantic annotation of corpora. This data has facilitated the development of automatic semantic role labeling systems based on supervised machine learning techniques. Chapter 3 presents the general principles of applying both supervised and unsupervised machine learning to this task, with a description of the standard stages and feature choices, as well as giving details of several specific systems. Recent advances include the use of joint inference to take advantage of context sensitivities, and attempts to improve performance by closer integration of the syntactic parsing task with semantic role labeling. Chapter 3 also discusses the impact the granularity of the semantic roles has on system performance. Having outlined the basic approach with respect to English, Chapter 4 goes on to discuss applying the same techniques to other languages, using Chinese as the primary example. Although substantial training data is available for Chinese, this is not the case for many other languages, and techniques for projecting English role labels onto parallel corpora are also presented. Table of Contents: Preface / Semantic Roles / Available Lexical Resources / Machine Learning for Semantic Role Labeling / A Cross-Lingual Perspective / Summary

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.

Ruslan Mitkov's highly successful Oxford Handbook of Computational Linguistics has been substantially revised and expanded in this second edition. Alongside updated accounts of the topics covered in the first edition, it includes 17 new chapters on subjects such as semantic role-labelling, text-to-speech synthesis, translation technology, opinion mining and sentiment analysis, and the application of Natural Language Processing in educational and biomedical contexts, among many others. The volume is divided into four parts that examine, respectively: the linguistic fundamentals of computational linguistics; the methods and resources used, such as statistical modelling, machine learning, and corpus annotation; key language processing tasks including text segmentation, anaphora resolution, and speech recognition; and the major applications of Natural Language Processing, from machine translation to author profiling. The book will be an essential reference for researchers and students in computational linguistics and Natural Language Processing, as well as those working in related industries.

The volume is divided into four sections: typology, syntax, discourse and phonology. Two of the typology papers study the structure and organization of category systems (Joseph Greenberg, Linda Schwartz); the third discusses language typology and universals from the perspective of language acquisition (Fred Eckman). The eight papers in the syntax section are of three types. Edith Moravcsik and James Tai discuss 'general' issues of linguistic theory/domain. Four papers (Mushira Eid, Michael Kac, Nancy Hedberg, Larry Hutchinson) address specific analyses and their implications from language-particular and theoretical perspectives. The papers by Deborah Dahl and Thomas Rindflesch relate theoretical concepts and analyses to natural language processing. In the section on discourse, the contributions by Anita Barry and Amy Sheldon deal with interpersonal conflict; George Yule discusses the selection between direct and indirect speech forms. Helga Delisle and Cynthia Clamons consider ways in which choices among, or variation in, some grammatical and semantic categories may be explainable on pragmatic and discourse grounds. The phonology papers are focused on two major themes: underspecification and borrowing. Four of the articles address the issue of underspecification in phonological representations (Daniel Dinnsen, Joseph Stemberger, Janet Bing, Gregory Iverson). In the other two papers questions of borrowing are discussed, in Nancy Stenson's contribution from a synchronic perspective, and in Gunter Schaarsmidt's paper from a historical one. The volume is completed by a subject index and a language index.

This book investigates how science can help mitigate social media's negative effects on communication and create more transparency.

This book provides a comprehensive foundation of distributional methods in computational modeling of meaning. It aims to build a common understanding of the theoretical and methodological foundations for students of computational linguistics, natural language processing, computer science, artificial intelligence, and cognitive science.