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L-attributed grammar

About: L-attributed grammar is a research topic. Over the lifetime, 2541 publications have been published within this topic receiving 58591 citations.


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Proceedings Article
Eugene Charniak1
04 Aug 1996
TL;DR: This paper presents results on a tree-bank grammar based on the Penn WaII Street Journal tree bank that outperforms other non-word-based statistical parsers/grammars on this corpus and outperforms parsers that consider the input as a string of tags and ignore the actual words of the corpus.
Abstract: By a "tree-bank grammar" we mean a context-free grammar created by reading the production rules directly from hand-parsed sentences in a tree bank. Common wisdom has it that such grammars do not perform we & though we know of no published data on the issue. The primary purpose of this paper is to show that the common wisdom is wrong. In particular, we present results on a tree-bank grammar based on the Penn WaII Street Journal tree bank. To the best of our knowledge, this grammar outperforms ah other non-word-based statistical parsers/grammars on this corpus. That is, it outperforms parsers that consider the input as a string of tags and ignore the actual words of the corpus.

338 citations

Journal ArticleDOI
TL;DR: A formal model of the mental representation of task languages is presented, a metalanguage for defining task-action grammars (TAG) that rewrite simple tasks into action specifications that make predictions about the relative learnability of different task language designs.
Abstract: A formal model of the mental representation of task languages is presented. The model is a metalanguage for defining task-action grammars (TAG): generative grammars that rewrite simple tasks into action specifications. Important features of the model are (a) Identification of the "simple-tasks" that users can perform routinely and that require no control structure; (b) Representation of simple-tasks by collections of semantic components reflecting a categorization of the task world; (c) Marking of tokens in rewrite rules with the semantic features of the task world to supply selection restrictions on the rewriting of simple-tasks into action specifications. This device allows the representation of family resemblances between individual task-action mappings. Simple complexity metrics over task-action grammars make predictions about the relative learnability of different task language designs. Some empirical support for these predictions is derived from the existing empirical literature on command language learning, and from two unreported experiments. Task-action grammars also provide designers with an analytic tool for exposing the configural properties of task languages.

328 citations

Book
01 Jun 1978
TL;DR: This book provides an introduction to basic concepts and techniques of syntactic pattern recognition and emphasizes fundamental and practical material rather than strictly theoretical topics.
Abstract: This book provides an introduction to basic concepts and techniques of syntactic pattern recognition. The presentation emphasizes fundamental and practical material rather than strictly theoretical topics, and numerous examples illustrate the principles. The subject is developed according to the following topics: introduction (background, patterns and pattern classes, approaches to pattern recognition, elements of a pattern recognition system, concluding remarks); elements of formal language theory (introduction; string grammars and languages; examples of pattern languages and grammars; equivalent context-free grammars; syntax-directed translations; deterministic, nondeterministic, and stochastic systems; concluding remarks); higher-dimensional grammars (introduction; tree grammars; web grammars; plex grammars; shape gammars; concluding remarks); recognition and translation of syntactic structures (introduction; string language recognizers; automata for simple syntax-directed translation; parsing in string languages; recognition of imperfect strings; tree automata; concluding remarks); stochastic grammars, languages, and recognizers (introduction; stochastic grammars and languages; consisting of stochastic context-free grammars; stochastic reocgnizers; stochastic syntax-directed translations; modified Cocke-Younger-Kasami parsing algorithm for stochastic errors of changed symbols; concluding remarks); and grammatical inference (introduction; inference of regular grammars; inference of context-free grammars; inference of tree grammars; inference of stochastic grammar; concluding remarks). 155 references, 93 figures, 4 tables. (RWR)

296 citations

Proceedings Article
04 Dec 2006
TL;DR: This paper presents a general-purpose inference algorithm for adaptor grammars, making it easy to define and use such models, and illustrates how several existing nonparametric Bayesian models can be expressed within this framework.
Abstract: This paper introduces adaptor grammars, a class of probabilistic models of language that generalize probabilistic context-free grammars (PCFGs). Adaptor grammars augment the probabilistic rules of PCFGs with "adaptors" that can induce dependencies among successive uses. With a particular choice of adaptor, based on the Pitman-Yor process, nonparametric Bayesian models of language using Dirichlet processes and hierarchical Dirichlet processes can be written as simple grammars. We present a general-purpose inference algorithm for adaptor grammars, making it easy to define and use such models, and illustrate how several existing nonparametric Bayesian models can be expressed within this framework.

292 citations

Journal ArticleDOI
James W. Thatcher1
TL;DR: The recognizable sets of value trees (pseudoterms) are shown to be exactly projections of sets of derivation trees of (extended) context-free grammars.

284 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202313
202220
20212
20202
20183
201739