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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.
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.
In the paper the lexical ambiguity resolution is presented. The paper is specifically focused on the processing of words, models of word recognition, context effect, trying to find an answer to how the reader-listener determines the contextually appropriate meaning of a word. Ambiguity resolution is analyzed and explored in two perspectives: the context in which the lexical items appear and the activation of all the meanings which an ambiguous word has. There is no clear-cut answer to lexical ambiguity resolution and there is a great debate about the role of the context in the activation of the meaning of ambiguous words. (Contains a bibliography.).
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 espe.
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.
Sets out state-of-the-art methodological and theoretical advancements to shed light on how bilingual speakers comprehend ambiguous information.