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This is an exciting time for Artificial Intelligence, and for Natural Language Processing in particular. Over the last five years or so, a newly revived spirit has gained prominence that promises to revitalize the whole field: the spirit of empiricism. This book introduces a new approach to the important NLP issue of automatic ambiguity resolution, based on statistical models of text. This approach is compared with previous work and proved to yield higher accuracy for natural language analysis. An effective implementation strategy is also described, which is directly useful for natural language analysis. The book is noteworthy for demonstrating a new empirical approach to NLP; it is essential reading for researchers in natural language processing or 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.
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.
Seminar paper from the year 2006 in the subject English Language and Literature Studies - Linguistics, grade: 1, University of Marburg (Fremdsprachliche Philologien), course: Proseminar Semantics, language: English, abstract: "Ambiguity is pervasive at all levels of analysis. It has been, is, and is likely to remain the key problem in natural language processing." (Gadzar 1993:161) This statement by Gerald Gadzar expresses the necessity to cope with the challenge of ambiguity resolution. As the phenomenon of ambiguity is widespread in human language, an interesting question would be: How could a machine be able to handle ambiguity while even humans have difficulties in solving such problems? This paper will first define the phenomenon of ambiguity and explain the different types of it. An interesting aspect will be the effect of garden path sentences.
Progress in computer animation has gained such a speed that, before long, computer-generated human faces and figures on screen will be indistinguishable from those of real humans. The potential both for scripted films and real-time interaction with users is enormous. However, in order to cope with this potential, these faces and figures must be guided by autonomous personality agents. This carefully arranged volume presents the state of the art in research and development in making synthetic actors more autonomous. The papers describe the different approaches and solutions developed by computer animation specialists, computer scientists, experts in AI, psychologists and philosophers, from leading laboratories world-wide. Finally, a bibliography comprising more than 200 entries enable further study.