Learning Context-Free Grammars from Partially Structured Examples

Sakakibara, Yasubumi; Muramatsu, Hidenori

doi:10.1007/978-3-540-45257-7_19

Cited by 27 publications

(17 citation statements)

References 5 publications

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“…Recently an enhancement to this algorithm was proposed [26], where learning CFG's was possible from partially structured sentences (some pairs of left and right parentheses missing). For example, a completely structured sample for the language L = {a m b m c n |m, n ≥ 1} is ((a(ab))b)(c(cc)), while some partially structure samples are (a(ab)b)(c(cc)), (a(ab)b)(ccc), and (aabb)(ccc).…”

Section: Related Workmentioning

confidence: 99%

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Inferring Context-Free Grammars for Domain-Specific Languages

Črepinšek

Mernik

Bryant

et al. 2005

Electronic Notes in Theoretical Computer Science

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In the area of programming languages, context-free grammars (CFGs) are of special importance since almost all programming languages employ CFG's in their design. Recent approaches to CFG induction are not able to infer context-free grammars for general-purpose programming languages. In this paper it is shown that syntax of a small domain-specific language can be inferred from positive and negative programs provided by domain experts. In our work we are using the genetic programming approach in grammatical inference. Grammar-specific heuristic operators and nonrandom construction of the initial population are proposed to achieve this task. Suitability of the approach is shown by examples where underlying context-free grammars are successfully inferred.

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Section: Related Workmentioning

confidence: 99%

“…But this is as hard as the original problem. Since using completely structured [25] or partially structured samples [26] are impractical we are using an approximation: frequent sequences. A string of symbols is called a frequent sequence if it appears at least θ times, where θ is some preset threshold.…”

Section: New Ideas and Approachesmentioning

confidence: 99%

Inferring Context-Free Grammars for Domain-Specific Languages

Črepinšek

Mernik

Bryant

et al. 2005

Electronic Notes in Theoretical Computer Science

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“…Two analysed methods that rely on evolutionary computing techniques are GA-based (Sakakibara and Muramatsu 2000) and LAgts (Briscoe 2000). In particular, the GA-based method uses Incremental parsing X Self-training X Co-training X a genetic algorithm for partitioning the set of non-terminals consistently with the given examples of sentences in order to eliminate unnecessary non-terminals and production rules from the initial (primitive) grammar.…”

Section: Underlying Computational Techniquesmentioning

confidence: 99%

“…The GA-based algorithm (Sakakibara and Muramatsu 2000) inductively learns context-free grammars from partially structured examples (positive and negative), i.e. only some partial information about the grammatical structure of the given examples is available.…”

Section: Ga-basedmentioning

confidence: 99%

A survey of grammatical inference methods for natural language learning

2011

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The high complexity of natural language and the huge amount of human and temporal resources necessary for producing the grammars lead several researchers in the area of Natural Language Processing to investigate various solutions for automating grammar generation and updating processes. Many algorithms for Context-Free Grammar inference have been developed in the literature. This paper provides a survey of the methodologies for inferring context-free grammars from examples, developed by researchers in the last decade. After introducing some preliminary definitions and notations concerning learning and inductive inference, some of the most relevant existing grammatical inference methods for Natural Language are described and classified according to the kind of presentation (if text or informant) and the type of information (if supervised, unsupervised, or semi-supervised). Moreover, the state of the art of the strategies for evaluation and comparison of different grammar inference methods is presented. The goal of the paper is to provide a reader with introduction to major concepts and current approaches in Natural Language Learning research.

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“…Some theoretical studies showed that context-free grammars (CFGs) and more restricted grammars are not learnable in polynomial time [15]. This is a reason that many studies on learning CFGs [3], [17] restrict the target grammars to subclasses of CFG or add some structural information to the samples for polynomial-time learning.…”

Section: Introductionmentioning

confidence: 99%

Search for Minimal and Semi-Minimal Rule Sets in Incremental Learning of Context-Free and Definite Clause Grammars

Imada

Nakamura

2010

IEICE Trans. Inf. & Syst.

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Keita IMADA†a) , Nonmember and Katsuhiko NAKAMURA †b) , Member SUMMARY This paper describes recent improvements to Synapse system for incremental learning of general context-free grammars (CFGs) and definite clause grammars (DCGs) from positive and negative sample strings. An important feature of our approach is incremental learning, which is realized by a rule generation mechanism called "bridging" based on bottom-up parsing for positive samples and the search for rule sets. The sizes of rule sets and the computation time depend on the search strategies. In addition to the global search for synthesizing minimal rule sets and serial search, another method for synthesizing semi-optimum rule sets, we incorporate beam search to the system for synthesizing semi-minimal rule sets. The paper shows several experimental results on learning CFGs and DCGs, and we analyze the sizes of rule sets and the computation time.

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Learning Context-Free Grammars from Partially Structured Examples

Cited by 27 publications

References 5 publications

Inferring Context-Free Grammars for Domain-Specific Languages

Inferring Context-Free Grammars for Domain-Specific Languages

A survey of grammatical inference methods for natural language learning

Search for Minimal and Semi-Minimal Rule Sets in Incremental Learning of Context-Free and Definite Clause Grammars

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