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This long-awaited revision offers a comprehensive introduction to natural language understanding with developments and research in the field today. Building on the effective framework of the first edition, the new edition gives the same balanced coverage of syntax, semantics, and discourse, and offers a uniform framework based on feature-based context-free grammars and chart parsers used for syntactic and semantic processing. Thorough treatment of issues in discourse and context-dependent interpretation is also provided.
In addition, this title offers coverage of two entirely new subject areas. First, the text features a new chapter on statistically-based methods using large corpora. Second, it includes an appendix on speech recognition and spoken language understanding. Also, the information on semantics that was covered in the first edition has been largely expanded in this edition to include an emphasis on compositional interpretation.
From a leading authority in artificial intelligence, this book delivers a synthesis of the major modern techniques and the most 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.
1. Introduction to Natural Language Understanding.
The Study of Language.
Applications of Natural Language Understanding.
Evaluating Language Understanding Systems.
The Different Levels of Language Analysis.
Representations and Understanding.
The Organization of Natural Language Understanding Systems.
2. Linguistic Background: An Outline of English Syntax.
The Elements of Simple Noun Phrases.
Verb Phrases and Simple Sentences.
Noun Phrases Revisited.
3. Grammars and Parsing.
Grammars and Sentence Structure.
What Makes a Good Grammar.
A Top-Down Parser.
A Bottom-Up Chart Parser.
Top-Down Chart Parsing.
Finite State Models and Morphological Processing.
Grammars and Logic Programming.
4. Features and Augmented Grammars.
Feature Systems and Augmented Grammars.
Some Basic Feature Systems for English.
Morphological Analysis and the Lexicon.
A Simple Grammar Using Features.
Parsing with Features.
Augmented Transition Networks.
Definite Clause Grammars.
Generalized Feature Systems and Unification Grammars.
5. Grammars for Natural Language.
Auxiliary Verbs and Verb Phrases.
Movement Phenomena in Language.
Handling Questions in Context-Free Grammars.
Noun Phrases and Relative Clauses.
The Hold Mechanism in ATN.
6. Toward Efficient Parsing.
Human Preferences in Parsing.
Encoding Uncertainty: Shift-Reduce Parsers.
A Deterministic Parser.
Techniques for Efficient Encoding of Ambiguity.
7. Ambiguity Resolution: Statistical Methods.
Basic Probability Theory.
Obtaining Lexical Probabilities.
Probabilistic Context-Free Grammars.
A Simple Context-Dependent Best-First Parser.
8. Semantics and Logical Form.
Semantics and Logical Form.
Word Senses and Ambiguity.
The Basic Logical Form Language.
Encoding Ambiguity in Logical Form.
Verbs and States in Logical Form.
Speech Acts and Embedded Sentences.
Defining Semantic Structure: Model Theory.
9. Linking Syntax and Semantics.
Semantic Interpretation and Compositionality.
A Simple Grammar and Lexicon with Semantic Interpretation.
Prepositional Phrases and Verb Phrases.
Lexicalized Semantic Interpretation and Semantic Roles.
Handling Simple Questions.
Semantic Interpretation Using Feature Unification
Generating Sentences from Logical Form.
10. Ambiguity Resolution.
Semantic Filtering Using Selectional Restrictions.
Statistical Word Sense Disambiguation.
Statistical Semantic Preferences.
Combining Approaches to Disambiguation.
11. Other Strategies for Semantic Interpretation.
Semantically-Directed Parsing Techniques.
12. Scoping and the Interpretation of Noun Phrases.
Definite Descriptions and Scoping.
A Method for Scoping While Parsing.
Co-Reference and Binding Constraints.
Relational Nouns and Nominalizations.
Other Problems in Semantics.
13. Knowledge Representation and Reasoning.
A Representation Based on FOPC.
Frames: Representing Stereotypical Information.
Handling Natural Language Quantification.
Time and Aspectual Classes of Verbs.
Automating Deduction in Logic-Based Representations.
Procedural Semantics and Question Answering.
Hybrid Knowledge Representations.
14. Local Discourse Context and Reference.
Defining Local Discourse Context and Discourse Entities.
A Simple Model of Anaphora Based on History Lists.
Pronouns and Centering.
Definite Reference and Sets.
15. Using World Knowledge.
Using World Knowledge: Establishing Coherence.
Matching Against Expectations.
Reference and Matching Expectations.
Using Knowledge About Action and Casualty.
Scripts: Understanding Stereotypical Situations.
Using Hierarchical Plans.
Using Knowledge About Rational Behavior.
16. Discourse Structure.
The Need for Discourse Structure.
Segmentation and Cue Phrases.
Discourse Structure and Reference.
Relating Discourse Structure and Inference.
Discourse Structure, Tense, and Aspect.
Managing the Attentional Stack.
17. Defining a Conversational Agent.
What's Necessary to Build a Conversational Agent?
Language as Multi-Agent Activity.
Representing Cognitive State: Beliefs.
Representing Cognitive State: Desires, Intentions, and Plans.
Speech Acts and Communicative Acts.
Communicative Acts and the Recognition of Intention.
The Source of Intention in Dialogue.
Recognizing Illocutionary Acts.
Discourse and Level Planning.
An Introduction to Logic and Model Theoretic Semantics.
Spoken Language. 0805303340T04062001