Automatic Ambiguity Resolution in Natural Language Processing

1. Front Matter

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2. Introduction

   Pages 1-10

3. Previous work on syntactic ambiguity resolution

   Pages 11-33

4. Loglinear models for ambiguity resolution

   Pages 35-49

5. Modeling new words

   Pages 51-70

6. Part-of-speech ambiguity

   Pages 71-96

7. Prepositional phrase attachment disambiguation

   Pages 97-108

8. Conclusions

   Pages 109-116

9. Back Matter

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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.

• Ambiguity Resolution
• Corpusbasierte Natürlichsprachliche Verarbeitung
• Empirische Künstiche Intelligenz
• artificial intelligence
• computational linguistics
• corpus-based NLP
• intelligence
• linguistics
• modeling
• natural language
• natural language analysi

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