Learning regular sets from queries and counterexamples
Information and Computation
On the Computational Complexity of Approximating Distributions by Probabilistic Automata
Machine Learning - Computational learning theory
When won't membership queries help?
Selected papers of the 23rd annual ACM symposium on Theory of computing
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Inductive Inference, DFAs, and Computational Complexity
AII '89 Proceedings of the International Workshop on Analogical and Inductive Inference
A study of grammatical inference
A study of grammatical inference
Part-of-speech induction from scratch
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Distributional phrase structure induction
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Corpus-based induction of syntactic structure: models of dependency and constituency
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
A Polynomial Algorithm for the Inference of Context Free Languages
ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
Another look at indirect negative evidence
CACLA '09 Proceedings of the EACL 2009 Workshop on Cognitive Aspects of Computational Language Acquisition
A learnable representation for syntax using residuated lattices
FG'09 Proceedings of the 14th international conference on Formal grammar
Formal and empirical grammatical inference
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts of ACL 2011
Towards dual approaches for learning context-free grammars based on syntactic concept lattices
DLT'11 Proceedings of the 15th international conference on Developments in language theory
A language theoretic approach to syntactic structure
MOL'11 Proceedings of the 12th biennial conference on The mathematics of language
Distributional learning of simple context-free tree grammars
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
Logical grammars, logical theories
LACL'12 Proceedings of the 7th international conference on Logical Aspects of Computational Linguistics
| Hi-index | 0.00 |
A central problem for NLP is grammar induction: the development of unsupervised learning algorithms for syntax. In this paper we present a lattice-theoretic representation for natural language syntax, called Distributional Lattice Grammars. These representations are objective or empiricist, based on a generalisation of distributional learning, and are capable of representing all regular languages, some but not all context-free languages and some non-context-free languages. We present a simple algorithm for learning these grammars together with a complete self-contained proof of the correctness and efficiency of the algorithm.