Algorithms for Learning Function Distinguishable Regular Languages

Fernau, Henning; Radl, Agnes

doi:10.1007/3-540-70659-3_6

Cited by 5 publications

(9 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As exhibited in [14] (based on the implementation of the 0-reversible language identification algorithm due to Angluin [3]), the algorithm sketched above can be implemented to run in nearly linear time, i.e., in time O(α −1 (2 n)2 n), where is the size of the input alphabet (this will become somewhat important in the following investigations), n is the total input size and α −1 is the inverse of the Ackermann function as defined by Tarjan [46]. In addition, "structural information" is sometimes provided to the learning algorithm (see [40] in the case of TDRL).…”

Section: Terminal Distinguishable Right-linear Languagesmentioning

confidence: 87%

“…Let us mention that the canonical objects required to define the convergence of the identification algorithm properly can be easily derived from the previous theorem and the canonical objects for TDRL-languages, as exhibited in [13,14]. Moreover, in this way, suitable characteristic samples for languages in TDRL − PC n GSTTfs can be obtained.…”

Section: Corollary 39mentioning

confidence: 99%

“…Since G is reduced, G is isomorphic to the canonical objects for TDRL defined in [13,14] in terms of product automata. Hence, the sketched procedure is indeed an identification algorithm for TDRL.…”

Section: Terminal Distinguishable Right-linear Languagesmentioning

confidence: 99%

“…Now, consider some enumeration of L i = L i ∩ { w ∈ Σ * | |w| ∈ M i } to the TDRL identification algorithm (of the ith component). Due to [14,Lemma 9], the finite automata sequence corresponding to the output sequence of right-linear grammars evoked by the enumeration of L i consists solely of sub-automata of the canonical finite automaton A of L i (where "canonical" refers to the definitions in [13,14]). Since the identification algorithm is conservative in the sense that it does not change its hypothesis as long as it is consistent with all the enumerated samples, and since the search space (the subautomata of A) is finite, the algorithm will converge, i.e., the output will be constant from a certain time step n i,0 onward, yielding someL i satisfying L i =L i ∩ { w ∈ Σ * | |w| ∈ M i }.…”

Section: Algorithm 35 (Decomposition Algorithm)mentioning

confidence: 99%

See 3 more Smart Citations

Parallel communicating grammar systems with terminal transmission

Fernau

2001

Acta Informatica

Self Cite

View full text Add to dashboard Cite

We introduce a new variant of PC grammar systems, called PC grammar systems with terminal transmission, PCGSTT for short. We show that right-linear centralized PCGSTT have nice formal language theoretic properties: they are closed under gsm mappings (in particular, under intersection with regular sets and under homomorphisms) and union; a slight variant is, in addition, closed under concatenation and star; their power lies between that of n-parallel grammars introduced by Wood and that of matrix languages of index n, and their relation to equal matrix grammars of degree n is discussed. We show that membership for these language classes is complete for NL. In a second part of the paper, we discuss questions concerning grammatical inference of these systems. More precisely, we show that PCGSTT whose component grammars are terminal distinguishable right-linear, a notion introduced by Radhakrishnan and Nagaraja in [33,34], are identifiable in the limit if certain data communication information is supplied in addition.

show abstract

Section: Terminal Distinguishable Right-linear Languagesmentioning

confidence: 87%

Section: Corollary 39mentioning

confidence: 99%

Section: Terminal Distinguishable Right-linear Languagesmentioning

confidence: 99%

Section: Algorithm 35 (Decomposition Algorithm)mentioning

confidence: 99%

See 2 more Smart Citations

Parallel communicating grammar systems with terminal transmission

Fernau

2001

Acta Informatica

Self Cite

View full text Add to dashboard Cite

show abstract

“…The proof is based on Theorem 8 in Fernau [2000b] (also in Fernau [2003]). The only difference between our setting and the setting of Fernau [2003] is that our extended f-distinguishable function does not need to have a finite codomain on T * , while f-distinguishable functions do.…”

Section: A Proof: Identifiability In the Limit Of Prefix Mark-up Encmentioning

confidence: 99%

Automatic information extraction from large websites

Crescenzi

Mecca

2004

J. ACM

146

118

View full text Add to dashboard Cite

Information extraction from websites is nowadays a relevant problem, usually performed by software modules called wrappers. A key requirement is that the wrapper generation process should be automated to the largest extent, in order to allow for large-scale extraction tasks even in presence of changes in the underlying sites. So far, however, only semi-automatic proposals have appeared in the literature.We present a novel approach to information extraction from websites, which reconciles recent proposals for supervised wrapper induction with the more traditional field of grammar inference. Grammar inference provides a promising theoretical framework for the study of unsupervised-that is, fully automatic-wrapper generation algorithms. However, due to some unrealistic assumptions on the input, these algorithms are not practically applicable to Web information extraction tasks.The main contributions of the article stand in the definition of a class of regular languages, called the prefix mark-up languages, that abstract the structures usually found in HTML pages, and in the definition of a polynomial-time unsupervised learning algorithm for this class. The article shows that, differently from other known classes, prefix mark-up languages and the associated algorithm can be practically used for information extraction purposes.A system based on the techniques described in the article has been implemented in a working prototype. We present some experimental results on known Websites, and discuss opportunities and limitations of the proposed approach.

show abstract

Learning Tree Languages from Text

Fernau

2002

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

We study the problem of learning regular tree languages from text. We show that the framework of function distinguishability, as introduced by the author in [Theoret. Comput. Sci. 290 (2003) 1679-1711], can be generalized from the case of string languages towards tree languages. This provides a large source of identifiable classes of regular tree languages. Each of these classes can be characterized in various ways. Moreover, we present a generic inference algorithm with polynomial update time and prove its correctness. In this way, we generalize previous works of Angluin, Sakakibara and ourselves. Moreover, we show that this way all regular tree languages can be approximately identified.

show abstract

Algorithms for Learning Function Distinguishable Regular Languages

Cited by 5 publications

References 12 publications

Parallel communicating grammar systems with terminal transmission

Parallel communicating grammar systems with terminal transmission

Automatic information extraction from large websites

Learning Tree Languages from Text

Contact Info

Product

Resources

About