Linguistics for the Age of AI

Name: Linguistics for the Age of AI
Brand: MIT Press
Price: 73.49 EUR
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Marjorie Mcshane Sergei Nirenburg(Autor*in)

MIT Press

Erschienen am 2. März 2021

448 Seiten

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Inhalt

Intro
Title Page
Copyright
Table of Contents
Acknowledgments
Setting the Stage
1. Our Vision of Linguistics for the Age of AI
1.1. What Is Linguistics for the Age of AI?
1.2. What Is So Hard about Language?
1.3. Relevant Aspects of the History of Natural Language Processing
1.4. The Four Pillars of Linguistics for the Age of AI
1.4.1. Pillar 1: Language Processing Capabilities Are Developed within an Integrated, Comprehensive Agent Architecture
1.4.2. Pillar 2: Modeling Is Human-Inspired in Service of Explanatory AI and Actionability
1.4.3. Pillar 3: Insights Are Gleaned from Linguistic Scholarship and, in Turn, Contribute to That Scholarship
1.4.3.1. Theoretical syntax
1.4.3.2. Psycholinguistics
1.4.3.3. Semantics
1.4.3.4. Pragmatics
1.4.3.5. Cognitive linguistics
1.4.3.6. Language evolution
1.4.4. Pillar 4: All Available Heuristic Evidence Is Incorporated When Extracting and Representing the Meaning of Language Inputs
1.4.4.1. Handcrafted knowledge bases for NLP
1.4.4.2. Using results from empirical NLP
1.5. The Goals of This Book
1.6. Deep Dives
1.6.1. The Phenomenological Stance
1.6.2. Learning
1.6.3. NLP and NLU: It's Not Either-Or
1.6.4. Cognitive Systems: A Bird's-Eye View
1.6.5. Explanation in AI
1.6.6. Incrementality in the History of NLP
1.6.7. Why Machine-Readable, Human-Oriented Resources Are Not Enough
1.6.8. Coreference in the Knowledge-Lean Paradigm
1.6.9. Dialog Act Detection
1.6.10. Grounding
1.6.11. More on Empirical NLP
1.6.12. Manual Corpus Annotation: Its Contributions, Complexities, and Limitations
1.7. Further Exploration
2. A Brief Overview of Natural Language Understanding by LEIAs
2.1. Theory, Methodology, and Strategy
2.2. A Warm-Up Example
2.3. Knowledge Bases
2.3.1. The Ontology
2.3.2. The Lexicon
2.3.3. Episodic Memory
2.4. Incrementality
2.5. The Stages of NLU and Associated Decision-Making
2.5.1. Decision-Making after Pre-Semantic Analysis
2.5.2. Decision-Making after Pre-Semantic Integration
2.5.3. Decision-Making after Basic Semantic Analysis
2.5.4. Decision-Making after Basic Coreference Resolution
2.5.5. Decision-Making after Extended Semantic Analysis
2.5.6. Decision-Making after Situational Reasoning
2.6. Microtheories
2.7. "Golden" Text Meaning Representations
2.8. Deep Dives
2.8.1. The LEIA Knowledge Representation Language versus Other Options
2.8.2. Issues of Ontology
2.8.3. Issues of Lexicon
2.8.4. Paraphrase in Natural Language and the Ontological Metalanguage
2.9. Further Exploration
3. Pre-Semantic Analysis and Integration
3.1. Pre-Semantic Analysis
3.2. Pre-Semantic Integration
3.2.1. Syntactic Mapping: Basic Strategy
3.2.2. Recovering from Production Errors
3.2.3. Learning New Words and Word Senses
3.2.4. Optimizing Imperfect Syn-Maps
3.2.5. Reambiguating Certain Syntactic Decisions
3.2.6. Handling Known Types of Parsing Errors
3.2.7. From Recovery Algorithm to Engineering Strategy
3.3. Managing Combinatorial Complexity
3.4. Taking Stock
3.5. Further Exploration
4. Basic Semantic Analysis
4.1. Modification
4.1.1. Recorded Property Values
4.1.2. Dynamically Computed Values for Scalar Attributes
4.1.3. Modifiers Explained Using Combinations of Concepts
4.1.4. Dynamically Computed Values for Relative Text Components
4.1.5. Quantification and Sets
4.1.6. Indirect Modification
4.1.7. Recap of Modification
4.2. Proposition-Level Semantic Enhancements
4.2.1. Modality
4.2.2. Aspect
4.2.3. Non-Modal, Non-Aspectual Matrix Verbs
4.2.4. Questions
4.2.5. Commands
4.2.6. Recap of Proposition-Level Semantic Enhancements
4.3. Multicomponent Entities Recorded as Lexical Constructions
4.3.1. Semantically Null Components of Constructions
4.3.2. Typical Uses of Null-Semming
4.3.3. Modification of Null-Semmed Constituents
4.3.4. Utterance-Level Constructions
4.3.5. Additional Knowledge Representation Requirements
4.3.6. Recap of Constructions
4.4. Indirect Speech Acts, Lexicalized
4.5. Nominal Compounds, Lexicalized
4.6. Metaphors, Lexicalized
4.6.1. Past Work on Metaphor
4.6.2. Conventional Metaphors
4.6.3. Copular Metaphors
4.6.4. Recap of Metaphors
4.7. Metonymies, Lexicalized
4.8. Ellipsis
4.8.1. Verb Phrase Ellipsis
4.8.2. Verb Phrase Ellipsis Constructions
4.8.3. Event Ellipsis: Aspectual + NPOBJECT
4.8.4. Event Ellipsis: Lexically Idiosyncratic
4.8.5. Event Ellipsis: Conditions of Change
4.8.6. Gapping
4.8.7. Head Noun Ellipsis
4.8.8. Recap of Ellipsis
4.9. Fragmentary Utterances
4.10. Nonselection of Optional Direct Objects
4.11. Unknown Words
4.11.1. Completely Unknown Words
4.11.2. Known Words in a Different Part of Speech
4.12. Wrapping Up Basic Semantic Analysis
4.13. Further Exploration
5. Basic Coreference Resolution
5.1. A Nontechnical Introduction to Reference Resolution
5.1.1. Definitions
5.1.2. An Example-Based Introduction
5.1.3. A Dozen Challenges
5.1.4. Special Considerations about Ellipsis
5.1.5. Wrapping Up the Introduction
5.2. Personal Pronouns
5.2.1. Resolving Personal Pronouns Using an Externally Developed Engine
5.2.2. Resolving Personal Pronouns Using Lexico-Syntactic Constructions
5.2.3. Semantically Vetting Hypothesized Pronominal Coreferences
5.2.4. Recap of Resolving Personal Pronouns during Basic Coreference Resolution
5.3. Pronominal Broad Referring Expressions
5.3.1. Resolving Pronominal Broad RefExes Using Constructions
5.3.2. Resolving Pronominal Broad RefExes in Syntactically Simple Contexts
5.3.3. Resolving Pronominal Broad RefExes Indicating Things That Must Stop
5.3.4. Resolving Pronominal Broad RefExes Using the Meaning of Predicate Nominals
5.3.5. Resolving Pronominal Broad RefExes Using Selectional Constraints
5.3.6. Recap of Resolving Pronominal Broad RefExes
5.4. Definite Descriptions
5.4.1. Definite Description Processing So Far: A Refresher
5.4.2. Definite Description Processing at This Stage
5.4.2.1. Rejecting coreference links with property value conflicts
5.4.2.2. Running reference-resolution meaning procedures listed in lexical senses
5.4.2.3. Establishing that a sponsor is not needed
5.4.2.4. Identifying bridging references
5.4.2.5. Creating sets as sponsors for plural definite descriptions
5.4.2.6. Identifying sponsors that are hypernyms or hyponyms of definite descriptions
5.4.3. Definite Description Processing Awaiting Situational Reasoning
5.4.4. Recap of Definite Description Processing at This Stage
5.5. Anaphoric Event Coreference
5.5.1. What is the Verbal/EVENT Head of the Sponsor?
5.5.2. Is There Instance or Type Coreference between the Events?
5.5.3. Is There Instance or Type Coreference between Objects in the VPs?
5.5.4. Should Adjuncts in the Sponsor Clause Be Included in, or Excluded from, the Resolution?
5.5.5. Should Modal and Other Scopers Be Included in, or Excluded from, the Resolution?
5.5.6. Recap of Anaphoric Event Coreference
5.6. Other Elided and Underspecified Events
5.7. Coreferential Events Expressed by Verbs
5.8. Further Exploration
6. Extended Semantic Analysis
6.1. Addressing Residual Ambiguities
6.1.1. The Objects Are Linked by a Primitive Property
6.1.2. The Objects Are Case Role Fillers of the Same Event
6.1.3. The Objects Are Linked by an Ontologically Decomposable Property
6.1.4. The Objects Are Clustered Using a Vague Property
6.1.5. The Objects Are Linked by a Short Ontological Path That Is Computed Dynamically
6.1.6. Reasoning by Analogy Using the TMR Repository
6.1.7. Recap of Methods to Address Residual Ambiguity
6.2. Addressing Incongruities
6.2.1. Metonymy
6.2.2. Preposition Swapping
6.2.3. Idiomatic Creativity
6.2.3.1. Detecting creative idiom use
6.2.3.2. Semantically analyzing creative idiom use
6.2.4. Indirect Modification Computed Dynamically
6.2.5. Recap of Treatable Types of Incongruities
6.3. Addressing Underspecification
6.3.1. Nominal Compounds Not Covered by Lexical Senses
6.3.2. Missing Values in Events of Change
6.3.3. Ungrounded and Underspecified Comparisons
6.3.4. Recap of Treatable Types of Underspecification
6.4. Incorporating Fragments into the Discourse Meaning
6.5. Further Exploration
7. Situational Reasoning
7.1. The OntoAgent Cognitive Architecture
7.2. Fractured Syntax
7.3. Residual Lexical Ambiguity: Domain-Based Preferences
7.4. Residual Speech Act Ambiguity
7.5. Underspecified Known Expressions
7.6. Underspecified Unknown Word Analysis
7.7. Situational Reference
7.7.1. Vetting Previously Identified Linguistic Sponsors for RefExes
7.7.2. Identifying Sponsors for Remaining RefExes
7.7.3. Anchoring the TMRs Associated with All RefExes in Memory
7.8. Residual Hidden Meanings
7.9. Learning by Reading
8. Agent Applications: The Rationale for Deep, Integrated NLU
8.1. The Maryland Virtual Patient System
8.1.1. Modeling Physiology
8.1.2. An Example: The Disease Model for GERD
8.1.3. Modeling Cognition
8.1.3.1. Learning new words and concepts through language interaction
8.1.3.2. Making decisions about action
8.1.4. An Example System Run
8.1.5. Visualizing Disease Models
8.1.5.1. Authoring instances of virtual patients
8.1.5.2. The knowledge about tests and interventions
8.1.5.3. Traces of system functioning
8.1.6. To What Extent Can MVP-Style Models Be Learned from Texts?
8.1.7. To What Extent Can Cognitive Models Be A Automatically Elicited from People?
8.2. A Clinician's Assistant for Flagging Cognitive Biases
8.2.1. Memory Support for Bias Avoidance
8.2.2. Detecting and Flagging Clinician Biases
8.2.3. Detecting and Flagging Patient Biases
8.3. LEIAs in Robotics
8.4. The Take-Home Message about Agent Applications
9. Measuring Progress
9.1. Evaluation Options-and Why the Standard Ones Don't Fit
9.2. Five Component-Level Evaluation Experiments
9.2.1. Nominal Compounding
9.2.2. Multiword Expressions
9.2.3. Lexical Disambiguation and the Establishment of the Semantic Dependency Structure
9.2.4. Difficult Referring Expressions
9.2.5. Verb Phrase Ellipsis
9.3. Holistic Evaluations
9.4. Final Thoughts
Epilogue
References
Index

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