Towards Incremental Learning of Mildly Context-Sensitive Grammars

Nakamura, Kotaro; Imada, Keita

doi:10.1109/icmla.2011.146

Cited by 2 publications

(3 citation statements)

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“…This section summarises experimental results of using our approach to induce ASGs. The approach was evaluated on several context-sensitive languages, including some languages drawn from a related paper targeting learning mildly context-sensitive (MCS) languages represented as linear indexed grammars (LIGs) (Nakamura and Imada 2011). The languages learned in this section are:…”

Section: Discussionmentioning

confidence: 99%

“…This is an indication that in real settings if the required level of choice is known a priori then inputting it in the initial language will save time, but in cases where it is unknown then unstratified ASGs are necessary. (Nakamura and Imada 2011) evaluate their LIG learner on each of the above languages other than subset-sum, graph-col and the variations of a n b n c n . LIGs make no distinction between the context-free part of the grammar so in their case the entire LIG was learned from scratch.…”

Section: Discussionmentioning

confidence: 99%

“…As shown in the other three cases, when starting from a more restricted CFG, our approach is able to use the existing grammar to find the complete ASG much faster than either method can learn the entire grammar from scratch. As LIGs are MCS, the approach in (Nakamura and Imada 2011) could not learn the subset-sum or graph-col languages.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Representing and Learning Grammars in Answer Set Programming

Law

Russo

Bertino

et al. 2019

AAAI

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In this paper we introduce an extension of context-free grammars called answer set grammars (ASGs). These grammars allow annotations on production rules, written in the language of Answer Set Programming (ASP), which can express context-sensitive constraints. We investigate the complexity of various classes of ASG with respect to two decision problems: deciding whether a given string belongs to the language of an ASG and deciding whether the language of an ASG is non-empty. Specifically, we show that the complexity of these decision problems can be lowered by restricting the subset of the ASP language used in the annotations. To aid the applicability of these grammars to computational problems that require context-sensitive parsers for partially known languages, we propose a learning task for inducing the annotations of an ASG. We characterise the complexity of this task and present an algorithm for solving it. An evaluation of a (prototype) implementation is also discussed.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%