Within the field of logic programming there have been numerous attempts to transform grammars into logic programs. This book describes a complementary approach that views logic programs as grammars and shows how this new presentation of the foundations of logic programming, based on the notion of proof trees, can enrich the field.
The authors' approach facilitates discussion of grammatical aspects of, and introduces new kinds of semantics for, definite programs. They survey relevant grammatical formalisms and provide a comprehensive introduction to the well-known attribute grammars and van Wijngaarden grammars. A formal comparison of definite programs to these grammars allows the authors to identify interesting grammatical concepts.
The book also includes a presentation of verification methods for definite programs derived from verification methods for attribute grammars, and an analysis of the occur-check problem as an example of how the grammatical view of logic programming can be applied.
Pierre Deransart is Research Director at INRIA-Rocquencourt, Le Chesnay Cedex, France. Jan Maluszynski is Professor in the Department of Computer and Information Science at Linköping University, Sweden.
Contents: Preliminaries. Foundations. Grammatical Extensions of Logic Programs. Attribute Grammars. Attribute Grammars and Logic Programming. Proof Methods. Study of Declarative Properties. The Occur-check Problem.
Intentions in Communication brings together major theorists from artificial intelligence and computer science, linguistics, philosophy, and psychology whose work develops the foundations for an account of the role of intentions in a comprehensive theory of communication. It demonstrates, for the first time, the emerging cooperation among disciplines concerned with the fundamental role of intention in communication.
The fourteen contributions in this book address central questions about the nature of intention as it is understood in theories of communication, the crucial role of intention recognition in understanding utterances, the use of principles of rational interaction in interpreting speech acts, the contribution of intonation contours to intention recognition, and the need for more general models of intention that support a view of dialogue as a collaborative activity.
Contributors: Michael E. Bratman, Philip R. Cohen, Hector J. Levesque, Martha E. Pollack, Henry Kautz, Andrew J. I. Jones, C. Raymond Perrault, Daniel Vanderveken, Janet Pierrehumbert, Julia Hirschberg, Richmond H. Thomason, Diane J Litman, James F. Allen, John R. Searle, Barbara J. Grosz, Candace L. Sidner, Herbert H. Clark and Deanna Wilkes-Gibbs. The book also includes commentaries by James F. Allen, W. A Woods, Jerry Morgan, Jerrold M. Sadock Jerry R. Hobbs, Kent Bach.
Intentions in Communication is included in the System Development Foundation Benchmark Series.
Constraint-based theories of grammar and grammar formalisms are becoming an increasingly widespread area of research in computational linguistics. Constraint-Based Grammar Formalisms provides the first rigorous mathematical and computational basis for this important area. It introduces new applications to both natural and computer languages and brings together Stuart Shieber's many contributions that have been at the core of developments ranging from the discovery of improved explanations of linguistic phenomena such as binding and coordination to the detailed mathematical analysis of constraint-solving and parsing in a variety of grammar formalisms.
This thorough examination of the theoretical and computational foundations of constraint-based grammars and applications to natural-language analysis is unique in several respects. Shieber's theoretical framework may be applied to a whole class of formalisms with properties that make it possible to define a general parsing algorithm for all members of the class, with results that provide essential guidance to the implementer of constraint-based language processing systems. Shieber also brings out new connections between grammatical categories and data types, and between constraint-based natural-language analysis and type inference in computer languages. These connections should be of increasing interest both to computational and theoretical linguists and to computer scientists.
Using sentence comprehension as a case study for all of cognitive science, David Townsend and Thomas Bever offer an integration of two major approaches, the symbolic-computational and the associative-connectionist. The symbolic-computational approach emphasizes the formal manipulation of symbols that underlies creative aspects of language behavior. The associative-connectionist approach captures the intuition that most behaviors consist of accumulated habits. The authors argue that the sentence is the natural level at which associative and symbolic information merge during comprehension.
The authors develop and support an analysis-by-synthesis model that integrates associative and symbolic information in sentence comprehension. This integration resolves problems each approach faces when considered independently. The authors review classic and contemporary symbolic and associative theories of sentence comprehension, and show how recent developments in syntactic theory fit well with the integrated analysis-by-synthesis model. They offer analytic, experimental, and neurological evidence for their model and discuss its implications for broader issues in cognitive science, including the logical necessity of an integration of symbolic and connectionist approaches in the field.
In this book Christian Jacquemin shows how the power of natural language processing (NLP) can be used to advance text indexing and information retrieval (IR). Jacquemin's novel tool is FASTR, a parser that normalizes terms and recognizes term variants. Since there are more meanings in a language than there are words, FASTR uses a metagrammar composed of shallow linguistic transformations that describe the morphological, syntactic, semantic, and pragmatic variations of words and terms. The acquired parsed terms can then be applied for precise retrieval and assembly of information.
The use of a corpus-based unification grammar to define, recognize, and combine term variants from their base forms allows for intelligent information access to, or "linguistic data tuning" of, heterogeneous texts. FASTR can be used to do automatic controlled indexing, to carry out content-based Web searches through conceptually related alternative query formulations, to abstract scientific and technical extracts, and even to translate and collect terms from multilingual material. Jacquemin provides a comprehensive account of the method and implementation of this innovative retrieval technique for text processing.
Recent attempts to unify linguistic theory and brain science have grown out of recognition that a proper understanding of language in the brain must reflect the steady advances in linguistic theory of the last forty years. The first Mind Articulation Project Symposium addressed two main questions: How can the understanding of language from linguistic research be transformed through the study of the biological basis of language? And how can our understanding of the brain be transformed through this same research? The best model so far of such mutual constraint is research on vision. Indeed, the two long-term goals of the Project are to make linguistics and brain science mutually constraining in the way that has been attempted in the study of the visual system and to formulate a cognitive theory that more strongly constrains visual neuroscience.
The papers in this volume discuss the current status of the cognitive/neuroscience synthesis in research on vision, whether and how linguistics and neuroscience can be integrated, and how integrative brain mechanisms can be studied through the use of noninvasive brain-imaging techniques.
Contributors: Noam Chomsky, Ann Christophe, Robert Desimone, Richard Frackowiak, Angela Friederici, Edward Gibson, Peter Indefrey, Masao Ito, Willem Levelt, Alec Marantz, Jacques Mehler, Yasushi Miyashita, David Poeppel, Franck Ramus, John Reynolds, Kensuke Sekihara, Hiroshi Shibasaki.
Parallel texts (bitexts) are a goldmine of linguistic knowledge, because the translation of a text into another language can be viewed as a detailed annotation of what that text means. Knowledge about translational equivalence, which can be gleaned from bitexts, is of central importance for applications such as manual and machine translation, cross-language information retrieval, and corpus linguistics. The availability of bitexts has increased dramatically since the advent of the Web, making their study an exciting new area of research in natural language processing. This book lays out the theory and the practical techniques for discovering and applying translational equivalence at the lexical level. It is a start-to-finish guide to designing and evaluating many translingual applications.
Until now, most discourse researchers have assumed that full semantic understanding is necessary to derive the discourse structure of texts. This book documents the first serious attempt to construct automatically and use nonsemantic computational structures for text summarization. Daniel Marcu develops a semantics-free theoretical framework that is both general enough to be applicable to naturally occurring texts and concise enough to facilitate an algorithmic approach to discourse analysis. He presents and evaluates two discourse parsing methods: one uses manually written rules that reflect common patterns of usage of cue phrases such as "however" and "in addition to"; the other uses rules that are learned automatically from a corpus of discourse structures. By means of a psycholinguistic experiment, Marcu demonstrates how a discourse-based summarizer identifies the most important parts of texts at levels of performance that are close to those of humans.
Marcu also discusses how the automatic derivation of discourse structures may be used to improve the performance of current natural language generation, machine translation, summarization, question answering, and information retrieval systems.
Natural language (NL) refers to human language--complex, irregular, diverse, with all its philosophical problems of meaning and context. Setting a new direction in AI research, this book explores the development of knowledge representation and reasoning (KRR) systems that simulate the role of NL in human information and knowledge processing.Traditionally, KRR systems have incorporated NL as an interface to an expert system or knowledge base that performed tasks separate from NL processing. As this book shows, however, the computational nature of representation and inference in NL makes it the ideal level for all tasks in an intelligent computer system. NL processing combines the qualitative characteristics of human knowledge processing with a computer¹s quantitative advantages, allowing for in-depth, systematic processing of vast amounts of information. The essays in this interdisciplinary book cover a range of implementations and designs, from formal computational models to large-scale NL processing systems.Contributors : Syed S. Ali, Bonnie J. Dorr, Karen Ehrlich, Robert Givan, Susan M. Haller, Sanda Harabagiu, Chung Hee Hwang, Lucja Iwanska, Kellyn Kruger, Naveen Mata, David A. McAllester, David D. McDonald, Susan W. McRoy, Dan Moldovan, William J. Rapaport, Lenhart Schubert, Stuart C. Shapiro, Clare R. Voss.
Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.