ICGI on

ICGI2023

Wed, 12 Jul 2023 00:00:00 +0000

This paper is concerned with the identification in the limit from positive data of substitutable context-free languages CFLs over infinite alphabets. [ClarkE07] showed that substitutable CFLs over finite alphabets are learnable in this learning paradigm. We show that substitutable CFLs generated by grammars whose production rules may have predicates that represent sets of potentially infinitely many terminal symbols in a compact manner are learnable if the terminal symbol sets represented by those predicates are learnable, under a certain condition. This can be seen as a result parallel to [ArgyrosDA2018]’s work (2018) that amplifies the query learnability of predicate classes to that of symbolic automata classes. Our result is the first that shows such amplification is possible for identifying some CFLs in the limit from positive data.

ICGI2023-Kanazawa

Wed, 12 Jul 2023 00:00:00 +0000

We consider an extension of distributional learning of context-free languages (from positive data and membership queries), where nonterminals are represented by extended regular expressions (allowi…

ICGI2021-Kambe

Mon, 23 Aug 2021 00:00:00 +0000

We propose an inside-outside algorithm for stochastic macro grammars. Our approach is based on types, which has been inspired by type-based approaches to reasoning about functional programs and higher-order grammars. By considering type derivations instead of ordinary word derivation sequences, we can naturally extend the standard inside-outside algorithm for stochastic context-free grammars to obtain the algorithm for stochastic macro grammars. We have implemented the algorithm and confirmed its effectiveness through an application to the learning of macro grammars.

ICGI2023-kanazawa

Mon, 23 Aug 2021 00:00:00 +0000

We consider a generalization of the “dual” approach to distributional learning of context-free grammars, where each nonterminal $A$ is associated with a string set $X_A$ characterized by a finite set $C$ of contexts. Rather than letting $X_A$ be the set of all strings accepted by all contexts in $C$ as in previous works, we allow more flexible uses of the contexts in $C$, using some of them positively (contexts that accept the strings in $X_A$) and others negatively (contexts that do not accept any strings in $X_A$). The resulting more general algorithm works in essentially the same way as before, but on a larger class of context-free languages.

ICGI2023-Suzuki

Mon, 23 Aug 2021 00:00:00 +0000

We propose a query learning algorithm for an extension of weighted finite automata (WFAs), named symbolic weighted finite automata (SWFAs), which can handle strings over infinite alphabets more efficiently. Based on the idea of symbolic finite automata, SWFAs generalize WFAs by allowing transitions to be functions from a possibly infinite alphabet to weights. Our algorithm can learn SWFAs if functions in transitions are also learnable by queries. We also investigate minimization and equivalence checking for SWFAs.

2012-01-ICGI

Sat, 01 Sep 2012 00:00:00 +0000

Semilinearity is widely held to be a linguistic invariant but, controversially, some linguistic phenomena in languages like Old Georgian and Yoruba seem to violate this constraint. In this paper we…