Context-sensitive grammar

About: Context-sensitive grammar is a research topic. Over the lifetime, 1938 publications have been published within this topic receiving 45911 citations. The topic is also known as: CSG.

Categorial Minimalist Grammar: From Generative Syntax To Logical Form

Abstract: We first recall some basic notions on minimalist grammars and on categorial grammars. Next we shortly introduce partially commutative linear logic, and our representation of minimalist grammars within this categorial system, the so-called categorial minimalist grammars. Thereafter we briefly present λμ-DRT (Discourse Representation Theory) an extension of λ-DRT (compositional DRT) in the framework of λμ calculus: it avoids type raising and derives different readings from a single semantic representation, in a setting which follows discourse structure. We run a complete example which illustrates the various structures and rules that are needed to derive a semantic representation from the categorial view of a transformational syntactic analysis.

On Characterizing Recursively Enumerable Languages by Insertion Grammars

Abstract: Insertion grammars have been introduced in [1] and their computational power has been studied in several places. In [7] it is proved that insertion grammars with weight at least 7 can characterize recursively enumerable languages (modulo a weak coding and an inverse morphism), and the question was formulated whether or not this result can be improved. In this paper, we come up with a positive answer to this question, by decreasing the weight of the insertion grammar used to 5. We also give a characterization of recursively enumerable languages in terms of right quotients of insertion languages.

Monotone AC-Tree automata

Abstract: We consider several questions about monotone AC-tree automata, a class of equational tree automata whose transition rules correspond to rules in Kuroda normal form of context-sensitive grammars. Whereas it has been proved that this class has a decision procedure to determine if, given a monotone AC-tree automaton, it accepts no terms, other important decidability or complexity results have not been well-investigated yet. In the paper, we prove that the membership problem for monotone AC-tree automata is PSPACE-complete. We then study the expressiveness of monotone AC-tree automata: precisely, we prove that the family of AC-regular tree languages is strictly subsumed in that of AC-monotone tree languages. The proof technique used in obtaining the above result yields the answers to two different questions, specifically that the family of monotone AC-tree languages is not closed under complementation, and that the inclusion problem for monotone AC-tree automata is undecidable.

Learning context-free grammar using improved tabular representation

Abstract: This paper describes an improved version of TBL algorithm [Y. Sakakibara, Learning context-free grammars using tabular representations, Pattern Recognition 38(2005) 1372-1383; Y. Sakakibara, M. Kondo, GA-based learning of context-free grammars using tabular representations, in: Proceedings of 16th International Conference in Machine Learning (ICML-99), Morgan-Kaufmann, Los Altos, CA, 1999] for inference of context-free grammars in Chomsky Normal Form. The TBL algorithm is a novel approach to overcome the hardness of learning context-free grammars from examples without structural information available. The algorithm represents the grammars by parsing tables and thanks to this tabular representation the problem of grammar learning is reduced to the problem of partitioning the set of nonterminals. Genetic algorithm is used to solve NP-hard partitioning problem. In the improved version modified fitness function and new delete specialized operator is applied. Computer simulations have been performed to determine improved a tabular representation efficiency. The set of experiments has been divided into 2 groups: in the first one learning the unknown context-free grammar proceeds without any extra information about grammatical structure, in the second one learning is supported by a partial knowledge of the structure. In each of the performed experiments the influence of partition block size in an initial population and the size of population at grammar induction has been tested. The new version of TBL algorithm has been experimentally proved to be not so much vulnerable to block size and population size, and is able to find the solutions faster than standard one.