Abstract
Approaches based on the idea generically called distributional learning have been making great success in the algorithmic learning of several rich subclasses of context-free languages and their extensions. Those language classes are defined by properties concerning string-context relation. In this paper, we present a distributional learning algorithm for conjunctive grammars with the k-finite context property (k-FCP) for each natural number k. We also compare our result with the closely related work by Clark et al. (JMLR 2010) [5] on learning some context-free grammars using contextual binary feature grammars (CBFGs). We prove that the context-free grammars targeted by their algorithm have the k-FCP. Moreover, we show that every exact CBFG has the k-FCP, too, while not all of them are learnable by their algorithm. Clark et al. conjectured a learning algorithm for exact CBFGs should exist. This paper answers their conjecture in a positive way.
Original language | English |
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Pages (from-to) | 359-374 |
Number of pages | 16 |
Journal | Journal of Computer and System Sciences |
Volume | 104 |
DOIs | |
Publication status | Published - 2019 Sep |
Externally published | Yes |
Keywords
- Conjunctive grammars
- Context-free grammars
- Contextual binary feature grammars
- Distributional learning
- Grammatical inference
- Learning theory
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Networks and Communications
- Computational Theory and Mathematics
- Applied Mathematics