Natural Language Understanding

Autorentext
James Allen is the John H. Dessaurer Professor of Computer Science at the University of Rochester. He has taught natural language processing to undergraduate and graduate students for 14 years. He is a fellow of the AAAI and was the recipient of the Presidential Young Investigator Award (1985-1989). In addition, Professor Allen was the Editor-in- Chief of Computational Linguistics from 1983-1993.

0805303340AB04062001

Klappentext
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.
0805303340B04062001

Inhalt


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.
Words. The Elements of Simple Noun Phrases. Verb Phrases and Simple Sentences. Noun Phrases Revisited. Adjective Phrases. Adverbial Phrases.

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. Gap Threading.

6. Toward Efficient Parsing.
Human Preferences in Parsing. Encoding Uncertainty: Shift-Reduce Parsers. A Deterministic Parser. Techniques for Efficient Encoding of Ambiguity. Partial Parsing.

7. Ambiguity Resolution: Statistical Methods.
Basic Probability Theory. Estimating Probabilities. Part-of-Speech Tagging. Obtaining Lexical Probabilities. Probabilistic Context-Free Grammars. Best-First Parsing. 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. Case Relations. 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.
Selectional Restrictions. Semantic Filtering Using Selectional Restrictions. Semantic Networks. Statistical Word Sense Disambiguation. Statistical Semantic Preferences. Combining Approaches to Disambiguation.

11. Other Strategies for Semantic Interpretation.
Grammatical Relations. Semantic Grammars. Template Matching. Semantically-Directed Parsing Techniques.

12. Scoping and the Interpretation of Noun Phrases.
Scoping Phenomena. Definite Descriptions and Scoping. A Method for Scoping While Parsing. Co-Reference and Binding Constraints. Adjective Phrases. Relational Nouns and Nominalizations. Other Problems in Semantics.

13. Knowledge Representation and Reasoning.

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