Automatic Ambiguity Resolution in Natural Language Processing [electronic resource] : An Empirical Approach / edited by Alexander Franz.

By: Franz, Alexander [editor.]Contributor(s): SpringerLink (Online service)Material type: TextTextLanguage: English Series: Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence: 1171Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 1996Description: XX, 164 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540495932Subject(s): Computer science | Artificial intelligence | Computer simulation | Statistics | Computer Science | Artificial Intelligence (incl. Robotics) | Simulation and Modeling | Mathematical Logic and Formal Languages | Statistics for Social Science, Behavorial Science, Education, Public Policy, and LawAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 LOC classification: Q334-342TJ210.2-211.495Online resources: Click here to access online
Contents:
Previous work on syntactic ambiguity resolution -- Loglinear models for ambiguity resolution -- Modeling new words -- Part-of-speech ambiguity -- Prepositional phrase attachment disambiguation -- Conclusions.
In: Springer eBooksSummary: 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.
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Previous work on syntactic ambiguity resolution -- Loglinear models for ambiguity resolution -- Modeling new words -- Part-of-speech ambiguity -- Prepositional phrase attachment disambiguation -- Conclusions.

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.

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