Synopses & Reviews
Actions are described by verbs whose subjects, objects, and other complements refer to various participants in those actions. What linguistic principles determine which participants are referred to by each component of a verb?
Many previous approaches to this problem have employed a set of thematic roles, such as agent and patient, to classify varieties of participants. The alternative developed here fits within the framework of Head-Driven Phrase Structure Grammar while utilizing typed feature structures, certain basic features of verb meaning, a hierarchical classification of verb meanings, and constraints from more general to more specific word classes. Relying on no special mechanisms or components of grammar, this book is unique in its ability to account for the observed range of verb types in human languages with a simple yet widely applicable set of principles.
Synopsis
Verbs describe actions, and their subject, objects, and other complements refer to various participants in those actions. For a verb referring to a certain type of action, what linguistic principles determine which participant is to be referred to by its subject, which by its object, and so forth? Many previous approaches to this problem have employed a set of thematic roles, such as agent and patient, to classify kinds of participants. The alternative presented in this book relies on certain basic features of verb meaning, such as causation, and on a hierarchical classification of verb meanings, to develop a simple but widely applicable set of principles that account for the observed range of verb types in human languages.
Description
Includes bibliographical references (p. 290-297) and index.
Table of Contents
Preface
1. Introduction
2. Thematic Roles and Lexical Entailments in Linking Theories
3. Lexical Semantic Relations and Structures
4. HPSG: A Brief Description and Some Revisions
5. Linking Constraints in the Lexical Hierarchy
6. Further Issues in Constraint-Based Linking
7. Conclusions and Prospects
References
Index