The language intersection problem for non-recursive context-free grammars

We prove that, given as input two context-free grammars, deciding non-emptiness of intersection of the two generated languages is PSPACE-complete if at least one grammar is non-recursive. The problem remains PSPACE-complete when both grammars are non-recursive and deterministic. Also investigated are generalizations of the problem to several context-free grammars, of which a certain number are non-recursive.     

Analyzing ambiguity of context-free grammars

It has been known since 1962 that the ambiguity problem for context-free grammars is undecidable. Ambiguity in context-free grammars is a recurring problem in language design and parser generation, as well as in applications where grammars are used as models of real-world physical structures.We observe that there is a simple linguistic characterization of the grammar ambiguity problem, and we show how to exploit this by presenting an ambiguity analysis framework based on conservative language approximations. As a concrete example, we propose a technique based on local regular approximations and grammar unfoldings. We evaluate the analysis using grammars that occur in RNA analysis in bioinformatics, and we demonstrate that it is sufficiently precise and efficient to be practically useful.     

Lower bounds for context-free grammars

Ellul, Krawetz, Shallit and Wang prove an exponential lower bound on the size of any context-free grammar generating the language of all permutations over some alphabet. We generalize their method and obtain exponential lower bounds for many other languages, among them the set of all squares of given length, and the set of all words containing each symbol at most twice.     

Nonterminal complexity of tree controlled grammars

This paper studies the nonterminal complexity of tree controlled grammars. It is proved that the number of nonterminals in tree controlled grammars without erasing rules leads to an infinite hierarchy of families of tree controlled languages, while every recursively enumerable language can be generated by a tree controlled grammar with erasing rules and at most nine nonterminals.     

Linearity and nondeletion on monadic context-free tree grammars

In this paper, subclasses of monadic context-free tree grammars (CFTGs) are compared. Since linear, nondeleting, monadic CFTGs generate the same class of string languages as tree adjoining grammars (TAGs), it is examined whether the restrictions of linearity and nondeletion on monadic CFTGs are necessary to generate the same class of languages.     

Scattered context grammars generate any recursively enumerable language with two nonterminals

By showing that two nonterminals are sufficient, we present the optimal lower bound on the number of nonterminals of scattered context grammars being able to generate any recursively enumerable language.     

An infinite hierarchy induced by depth synchronization

Depth-synchronization measures the number of parallel derivation steps in a synchronized context-free (SCF) grammar. When not bounded by a constant the depth-synchronization measure of an SCF grammar is at least logarithmic and at most linear with respect to the word length. Languages with linear depth-synchronization measure and languages with a depth-synchronization measure in between logarithmic and linear are proven to exist. This gives rise to a strict infinite hierarchy within the family of SCF (and ET0L) languages.     

Simple restriction in context-free rewriting

Many rewriting systems with context-free productions and with controlled derivations have been studied. On one hand, these systems preserve the simplicity of applications of context-free productions and, on the other hand, they increase the generative power to cover more aspects of natural and programming languages. However, with λ-productions, many of these systems are computationally complete. It gives rise to a natural question of what are the simplest restrictions of the derivation process of context-free grammars to obtain the universal power. In this paper, we present such a simple restriction introducing so-called restricted context-free rewriting systems. These systems are context-free grammars with a function assigning a nonterminal coupled with + or − to each nonterminal. A production is applicable if it is applicable as a context-free production and if the symbol assigned to the left-hand side of the production is coupled with +, then this symbol has to appear in the sentential form, while if coupled with −, it must not appear in the sentential form. This restriction is simpler than most of the other restrictions, since the context conditions are assigned to nonterminals, not to productions, and their type is the simplest possible – a nonterminal.     

On derivation languages corresponding to context-free grammars

Summary A derivation language associated with a context-free grammar is the set of all terminating derivations. Hierarchy and closure properties of these languages are considered. In addition to the formerly known solvability of the emptiness and finiteness problems the equivalence problem is shown to be solvable for derivation languages.     

Fractal encoding of context-free grammars in connectionist networks

Connectionist network learning of context-free languages has so far been applied only to very simple cases and has often made use of an external stack. Learning complex context-free languages with a homogeneous neural mechanism looks like a much harder problem. The current paper takes a step toward solving this problem by analyzing context-free grammar computation (without addressing learning) in a class of analog computers called dynamical automata, which are naturally implemented in connectionist networks. The result is a widely applicable method of using fractal sets to organize infinite-state computations in a bounded state space. An appealing consequence is the development of parameter-space maps, which locate various complex computers in spatial relationships to one another. An example suggests that such a global perspective on the organization of the parameter space may be helpful for solving the hard problem of getting connectionist networks to learn complex grammars from examples.     

Learning context-free grammars using tabular representations

We present a novel algorithm using new hypothesis representations for learning context-free grammars from a finite set of positive and negative examples. We propose an efficient hypothesis representation method which consists of a table-like data structure similar to the parse table used in efficient parsing algorithms for context-free grammars such as Cocke-Younger-Kasami algorithm. By employing this representation method, the problem of learning context-free grammars from examples can be reduced to the problem of partitioning the set of nonterminals. We use genetic algorithms for solving this partitioning problem. Further, we incorporate partially structured examples to improve the efficiency of our learning algorithm, where a structured example is represented by a string with some parentheses inserted to indicate the shape of the derivation tree of the unknown grammar. We demonstrate some experimental results using these algorithms and theoretically analyse the completeness of the search space using the tabular method for context-free grammars.