Topic
Tree-adjoining grammar
About: Tree-adjoining grammar is a research topic. Over the lifetime, 2491 publications have been published within this topic receiving 57813 citations.
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TL;DR: The paper demonstrates that the computational restrictiveness imposed by Tree Adjoining Grammar provides important insights into the nature of human grammatical knowledge.
12 citations
TL;DR: A hypergraph-generating system, called HRNCE grammars, which is structurally simple and descriptively powerful, which can generate all recursively enumerable languages.
12 citations
01 Jan 2010
TL;DR: 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.
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.
12 citations
27 Mar 1995
TL;DR: The decidability of the generation problem for those unification grammars which are based on context-free phrase structure rule skeletons, like e.g. LFG and PATR-II is proved.
Abstract: In this paper, we prove the decidability of the generation problem for those unification grammars which are based on context-free phrase structure rule skeletons, like e.g. LFG and PATR-II. The result shows a perhaps unexpected asymmetry, since it is valid also for those unification grammars whose parsing problem is undecidable, e.g. grammars which do not satisfy the off-line parsability constraint. The general proof is achieved by showing that the space of the derivations which have to be considered in order to decide the problem for a given input is always restricted to derivations whose length is limited by some fixed upper bound which is determined relative to the "size" of the input.
12 citations