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Natural Language Understanding (2nd Edition)

Foundations of Statistical Natural Language Processing (FSNLP)

By Casper Goldberg

Natural Language Understanding (2nd Edition)

     Since its publication, Natural Language Understanding has become one of the standards in the world in Natural Language Processing. Serving as an informative introduction to computational linguistics, this book presents the theory, algorithms and representations fundamental to industry and academia. Although dated in some areas, this book delivers a mixture of modern techniques and current research in natural language processing. The approach is unique in its coverage of semantic interpretation and discourse alongside the foundational material in syntactic processing. This edition covered the latest research developments of the time (1995)

including context-free grammars and associated framework, chart parsers used in syntactic and semantic processing.
     This book offers coverage of two new subjects: statistically based methods using large corpora and an appendix on speech recognition and spoken language understanding. This issue also cover semantics with an emphasis on compositional interpretation. The author explains how to build natural language systems and includes examples to help define domain specific words such as “anaphora”. You can find more information on this book at
     The author James Allen, the John H. Dessaurer Professor of Computer Science at the University of Rochester, has taught natural language processing for over a decade. Visit his home page at

Table of Contents
1. Introduction to Natural Language Understanding (NLU) - Study of language, applications of NLU, language analysis, representations and organizations of NLU systems.
2. Linguistic Background: An Outline of English Syntax - Words, noun, verb and adjective phrases and simple sentences.
3. Grammars and Parsing - Sentence structure, finite state models, parser, morphological processing and logic programming.
4. Features and Augmented Grammars - Morphological analysis and the lexicon, parsing with features, definite clause and unification grammars and augmented transition networks.
5. Grammars for Natural Language - Auxiliary verbs and verb phrases, context-free grammars, noun phrases and relative clauses.
6. Toward Efficient Parsing - Human preferences in parsing, shift-reduce and deterministic parser, encoding uncertainty and ambiguity and partial parsing.
7. Ambiguity Resolution - Statistical methods, basic probability theory, lexical probabilities, part-of-speech tagging, and a simple context-

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