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

• 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. Knowledge Representation. 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 Descriptions. Definite Reference and Sets. Ellipsis. Surface Anaphora. 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. Action-Effect-Based Reasoning. 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. An Example. 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. Planning. Communicative Acts. Communicative Acts and the Recognition of Intention. The Source of Intention in Dialogue. Recognizing Illocutionary Acts. Discourse and Level Planning. Appendices. An Introduction to Logic and Model Theoretic Semantics. Symbolic Computation. Spoken Language. 0805303340T04062001

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

• 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. Knowledge Representation. 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 Descriptions. Definite Reference and Sets. Ellipsis. Surface Anaphora. 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. Action-Effect-Based Reasoning. 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. An Example. 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. Planning. Communicative Acts. Communicative Acts and the Recognition of Intention. The Source of Intention in Dialogue. Recognizing Illocutionary Acts. Discourse and Level Planning. Appendices. An Introduction to Logic and Model Theoretic Semantics. Symbolic Computation. Spoken Language. 0805303340T04062001

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

• 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. Knowledge Representation. 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 Descriptions. Definite Reference and Sets. Ellipsis. Surface Anaphora. 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. Action-Effect-Based Reasoning. 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. An Example. 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. Planning. Communicative Acts. Communicative Acts and the Recognition of Intention. The Source of Intention in Dialogue. Recognizing Illocutionary Acts. Discourse and Level Planning. Appendices. An Introduction to Logic and Model Theoretic Semantics. Symbolic Computation. Spoken Language. 0805303340T04062001