Resolving Semantic Ambiguity arrrays the work of leading theorists on the issues surrounding the meaning and interpretation of ambiguous text. The chapters are organized around three major themes: (1) retrieval, (2) representation of words, and (3) text as a context. The book offers a number of new challenges to the role of context in language processing, some striking new evidence on the repetition of homographs in different contexts, and new approaches to resolution capable of being incorporated into either modular or network models. In several papers the problem of ambiguity is extended to include the problem of weak ambiguity and understanding text themes. The book provides a unique starting point for researchers approaching the problems of meaning in cognitive science, psychology, and computational linguistics.

Semantic interpretation and the resolution of ambiguity presents an important advance in computer understanding of natural language. While parsing techniques have been greatly improved in recent years, the approach to semantics has generally improved in recent years, the approach to semantics has generally been ad hoc and had little theoretical basis. Graeme Hirst offers a new, theoretically motivated foundation for conceptual analysis by computer, and shows how this framework facilitates the resolution of lexical and syntactic ambiguities. His approach is interdisciplinary, drawing on research in computational linguistics, artificial intelligence, montague semantics, and cognitive psychology.

This volume is concerned with how ambiguity and ambiguity resolution are learned, that is, with the acquisition of the different representations of ambiguous linguistic forms and the knowledge necessary for selecting among them in context. Schütze concentrates on how the acquisition of ambiguity is possible in principle and demonstrates that particular types of algorithms and learning architectures (such as unsupervised clustering and neural networks) can succeed at the task. Three types of lexical ambiguity are treated: ambiguity in syntactic categorisation, semantic categorisation, and verbal subcategorisation. The volume presents three different models of ambiguity acquisition: Tag Space, Word Space, and Subcat Learner, and addresses the importance of ambiguity in linguistic representation and its relevance for linguistic innateness.

Sets out state-of-the-art methodological and theoretical advancements to shed light on how bilingual speakers comprehend ambiguous information.

The most frequently used words in English are highly ambiguous; for example, Webster's Ninth New Collegiate Dictionary lists 94 meanings for the word "run" as a verb alone. Yet people rarely notice this ambiguity. Solving this puzzle has commanded the efforts of cognitive scientists for many years. The solution most often identified is "context": we use the context of utterance to determine the proper meanings of words and sentences. The problem then becomes specifying the nature of context and how it interacts with the rest of an understanding system. The difficulty becomes especially apparent in the attempt to write a computer program to understand natural language. Lexical ambiguity resolution (LAR), then, is one of the central problems in natural language and computational semantics research. A collection of the best research on LAR available, this volume offers eighteen original papers by leading scientists. Part I, Computer Models, describes nine attempts to discover the processes necessary for disambiguation by implementing programs to do the job. Part II, Empirical Studies, goes into the laboratory setting to examine the nature of the human disambiguation mechanism and the structure of ambiguity itself. A primary goal of this volume is to propose a cognitive science perspective arising out of the conjunction of work and approaches from neuropsychology, psycholinguistics, and artificial intelligence--thereby encouraging a closer cooperation and collaboration among these fields. Lexical Ambiguity Resolution is a valuable and accessible source book for students and cognitive scientists in AI, psycholinguistics, neuropsychology, or theoretical linguistics.

Resolving Semantic Ambiguity arrrays the work of leading theorists on the issues surrounding the meaning and interpretation of ambiguous text. The chapters are organized around three major themes: (1) retrieval, (2) representation of words, and (3) text as a context. The book offers a number of new challenges to the role of context in language processing, some striking new evidence on the repetition of homographs in different contexts, and new approaches to resolution capable of being incorporated into either modular or network models. In several papers the problem of ambiguity is extended to include the problem of weak ambiguity and understanding text themes. The book provides a unique starting point for researchers approaching the problems of meaning in cognitive science, psychology, and computational linguistics.

Contributors present the latest research in a relatively new area in the study of comprehension and discourse processes--lexically ambiguous words, how they are accessed, and how meaning is derived. Investigators describe the current state of knowledge and theory regarding the role that words play in the comprehension.

Introduces the major elements of semantics in a simple, step-by-step fashion. Sections of explanation and examples are followed by practice exercises with answers and comment provided.

Natural Language Processing and Information Retrieval is a textbook designed to meet the requirements of engineering students pursuing undergraduate and postgraduate programs in computer science and information technology. The book attempts to bridge the gap between theory and practice and would also serve as a useful reference for professionals and researchers working on language-related projects.

Research concerning structure and processing in the mental lexicon has achieved central prominence within cognitive psychology and psycholinguistics. Historically, however, much of the research on the lexicon focussed not on its role in language comprehension, but as a medium for studying semantic memory. This picture has changed in recent years, with much more research examining the role of lexical processes and output in language comprehension.Gathered together in this volume is the work of some of those researchers who are responsible for this shift of emphasis. Chapters deal with the role of sentence contexts in word recognition, processes involved in the activation and enhancement of lexical information, and the interaction of lexical and syntactic information in sentence processing. A wide range of theoretical and empirical issues relating to language understanding are discussed.