Abstract:
In order to extend the potential of application of the syntactic approach to pattern recognition, the efficient use of models capable of describing context-sensitive structural relationships is needed. Moreover, the ability to learn such models from examples is interesting to automate as much as possible the development of applications. In this paper, a new formalism that permits to describe a non-trivial class of context-sensitive languages, the Augmented Regular Expressions (AREs), is introduced. AREs augment the descriptive power of regular expressions by including a set of constraints that involve the number of instances of the operands of the star operations in each string of the language. Likewise, algorithms are given to infer AREs from string examples and to recognize language strings by AREs. The method for learning AREs consists of a regular grammatical inference step, aimed at obtaining a regular superset of the target language, followed by a constraint induction process, which reduces the extension of the inferred language transforming it into a context-sensitive one. Hence, this two-step approach avoids the difficulty of learning context-sensitive grammars directly from the data. The method for recognizing language strings is also splitted in two stages: matching the underlying regular expression and checking that the resulting star instances satisfy the constraints. 1.
Citations
|
2771
|
Introduction to Automata Theory, Language, and Computation
– Hopcroft, Ullman
- 1979
|
|
624
|
Language identification in the limit
– Gold
- 1967
|
|
356
|
Formal Languages
– Salomaa
- 1973
|
|
254
|
Inductive inference: theory and methods
– Angluin, Smith
- 1983
|
|
235
|
Inductive inference of formal languages from positive data
– Angluin
- 1980
|
|
210
|
Transition Network Grammars for Natural Language Analysis
– Woods
- 1970
|
|
176
|
Finding patterns common to a set of strings
– Angluin
- 1980
|
|
173
|
Learning and extracting finite state automata with second-order-recurrent neural networks
– Giles, Miller, et al.
- 1992
|
|
172
|
Complexity of automaton identification from given data
– Gold
- 1978
|
|
169
|
Syntactic Pattern Recognition and Applications
– Fu
- 1982
|
|
70
|
Ecient learning of context-free grammars from positive structural examples
– Sakakibara
- 1992
|
|
44
|
A note on the number of queries needed to identify regular languages
– Angluin
- 1981
|
|
32
|
Regular Grammatical Inference from positive and negative samples by genetic search: the GIG method
– Dupont
- 1994
|
|
24
|
A Solution of the Syntactic Induction-Inference Problem for Regular Languages
– Pao, Carr
- 1978
|
|
22
|
Grammatical inference for even linear languages based on control sets
– Takada
- 1988
|
|
21
|
Identifying regular languages in polynomial time
– Oncina, Garca
- 1992
|
|
19
|
Switching and Finite Automata Theory (2nd ed
– Kohavi
- 1978
|
|
17
|
Grammatical Inference
– Miclet
- 1990
|
|
13
|
A hierarchy of language families learnable by regular language learners
– Takada
- 1994
|
|
12
|
Inference of even linear grammars and its application to picture description languages
– Radhakrishnan, Nagaraja
- 1988
|
|
12
|
Active grammatical inference: a new learning methodology
– Sanfeliu, Alquézar
- 1994
|
|
10
|
Grammatical inference: an introductory survey
– Vidal
- 1994
|
|
8
|
Incremental grammatical inference from positive and negative data using unbiased finite state automata
– Alquezar, Sanfeliu
- 1995
|
|
8
|
Identification of pattern languages from examples and queries
– Marron, Ko
- 1987
|
|
8
|
Modelling (sub)string-length-based constraints through a Grammatical Inference Method
– Rulot, Vidal
- 1987
|
|
6
|
Data-driven inductive inference of finite-state automata
– Gregor
- 1994
|
|
6
|
Efficient regular grammatical inference; techniques by the use of partial similarities and their logical relationships
– Kudo, Shimbo
- 1988
|
|
5
|
An algebraic framework to represent finite-state machines in single-layer recurrent neural networks
– Alquezar, Sanfeliu
- 1995
|
|
4
|
Delay logic and finite state machines
– Arden
- 1961
|
|
4
|
Learning general context-free grammars from positive structural samples and negative strings
– Garcia, Oncina
- 1993
|
|
3
|
and K.S.Fu, "Inference for transition network grammars
– Chou
- 1976
|
|
3
|
Remarks on the structural grammatical inference problem for context-free grammars
– MÄKINEN
- 1992
|
|
