On the relationship between lexical semantics and syntax for the inference of context-free grammars

Authors:
Tim Oates;Tom Armstrong;Justin Harris;Mark Nejman
Affiliations:
Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD;Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD;Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD;Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD
Venue:
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Year:
2004

Citing 11
Cited 4

Learning regular sets from queries and counterexamples

Information and Computation
Learning simple concepts under simple distributions

SIAM Journal on Computing
Efficient learning of context-free grammars from positive structural examples

Information and Computation
Inference of Reversible Languages

Journal of the ACM (JACM)
Inferring pure context-free languages from positive data

Acta Cybernetica
Meaning Helps Learning Syntax

ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
Learning k-Reversible Context-Free Grammars from Positive Structural Examples

ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Stochastic Inference of Regular Tree Languages

ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
PAC Learning with Simple Examples

STACS '96 Proceedings of the 13th Annual Symposium on Theoretical Aspects of Computer Science
Learning words from sights and sounds: a computational model

Learning words from sights and sounds: a computational model
Grounding knowledge in sensors: unsupervised learning for language and planning

Grounding knowledge in sensors: unsupervised learning for language and planning

Learning Meaning Before Syntax

ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
Processing methods for contextual coherence interpretation related to reputation

ISCIT'09 Proceedings of the 9th international conference on Communications and information technologies
An Architecture for Bootstrapping Lexical Semantics and Grammatical Structures

WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Inferring grammars for mildly context sensitive languages in polynomial-time

ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Context-free grammars cannot be identified in the limit from positive examples (Gold 1967), yet natural language grammars are more powerful than context-free grammars and humans learn them with remarkable ease from positive examples (Marcus 1993). Identifiability results for formal languages ignore a potentially powerful source of information available to learners of natural languages, namely, meanings. This paper explores the learnability of syntax (i.e. context-free grammars) given positive examples and knowledge of lexical semantics, and the learnability of lexical semantics given knowledge of syntax. The long-term goal is to develop an approach to learning both syntax and semantics that bootstraps itself, using limited knowledge about syntax to infer additional knowledge about semantics, and limited knowledge about semantics to infer additional knowledge about syntax.