The Experts below are selected from a list of 303 Experts worldwide ranked by ideXlab platform

Timothy A. D. Fowler - One of the best experts on this subject based on the ideXlab platform.

  • MOL - The generative power of probabilistic and weighted Context-Free Grammars
    Lecture Notes in Computer Science, 2011
    Co-Authors: Timothy A. D. Fowler
    Abstract:

    Over the last decade, probabilistic parsing has become the standard in the parsing literature where one of the purposes of those probabilities is to discard unlikely parses. We investigate the effect that discarding low probability parses has on both the weak and strong generative power of Context-Free Grammars. We prove that probabilistic Context-Free Grammars are more powerful than their non-probabilistic counterparts but in a way that is orthogonal to the Chomsky hierarchy. In particular, we show that the increase in power cannot be used to model any dependencies that discrete Context-Free Grammars cannot.

Francisco Casacuberta - One of the best experts on this subject based on the ideXlab platform.

  • Statistical estimation of stochastic Context-Free Grammars
    Pattern Recognition Letters, 1995
    Co-Authors: Francisco Casacuberta
    Abstract:

    The Inside-Outside Algorithm is used for the estimation of the probabilistic component of Stochastic Context-Free Grammars in Chomsky Normal Form. This algorithm can also be used for proper Context-Free Grammars with or without certain types of single rules by using a modification of well-known transformations on Context-Free Grammars. Moreover, when the proper grammar without single rules is linear, specific algorithms for probabilistic estimation can be derived from the Inside-Outside Algorithm and the method proposed here.

Yasubumi Sakakibara - One of the best experts on this subject based on the ideXlab platform.

  • Learning Context-Free Grammars using tabular representations
    Pattern Recognition, 2005
    Co-Authors: Yasubumi Sakakibara
    Abstract:

    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.

  • efficient learning of context free Grammars from positive structural examples
    Information & Computation, 1992
    Co-Authors: Yasubumi Sakakibara
    Abstract:

    In this paper, we introduce a new normal form for Context-Free Grammars, called reversible Context-Free Grammars, for the problem of learning Context-Free Grammars from positive-only examples. A Context-Free grammar G = (N, @S, P, S) is said to be reversible if (1) A -> @a and B -> @a in P implies A = B and (2) A -> @[email protected] and A -> @[email protected] in P implies B = C. We show that the class of reversible Context-Free Grammars can be identified in the limit from positive samples of structural descriptions and there exists an efficient algorithm to identify them from positive samples of structural descriptions, where a structural description of a Context-Free grammar is an unlabelled derivation tree of the grammar. This implies that if positive structural examples of a reversible Context-Free grammar for the target language are available to the learning algorithm, the full class of Context-Free languages can be learned efficiently from positive samples.

Sherzod Turaev - One of the best experts on this subject based on the ideXlab platform.

  • Binary Context-Free Grammars
    Symmetry, 2020
    Co-Authors: Sherzod Turaev, Rawad Abdulghafor, Ali Amer Alwan, Ali Abd Almisreb, Yonis Gulzar
    Abstract:

    A binary grammar is a relational grammar with two nonterminal alphabets, two terminal alphabets, a set of pairs of productions and the pair of the initial nonterminals that generates the binary relation, i.e., the set of pairs of strings over the terminal alphabets. This paper investigates the binary Context-Free Grammars as mutually controlled Grammars: two Context-Free Grammars generate strings imposing restrictions on selecting production rules to be applied in derivations. The paper shows that binary Context-Free Grammars can generate matrix languages whereas binary regular and linear Grammars have the same power as Chomskyan regular and linear Grammars.

  • static watson crick context free Grammars
    International Journal of Online and Biomedical Engineering (iJOE), 2019
    Co-Authors: Wan Heng Fong, Aqilahfarhana Abdul Rahman, Nor Haniza Sarmin, Sherzod Turaev
    Abstract:

    Sticker systems and Watson-Crick automata are two modellings of DNA molecules in DNA computing. A sticker system is a computational model which is coded with single and double-stranded DNA molecules; while Watson-Crick automata is the automata counterpart of sticker system which represents the biological properties of DNA. Both of these models use the feature of Watson-Crick complementarity in DNA computing. Previously, the grammar counterpart of the Watson-Crick automata have been introduced, known as Watson-Crick Grammars which are classified into three classes: Watson-Crick regular Grammars, Watson-Crick linear Grammars and Watson-Crick Context-Free Grammars. In this research, a new variant of Watson-Crick grammar called a static Watson-Crick Context-Free grammar, which is a grammar counterpart of sticker systems that generates the double-stranded strings and uses rule as in Context-Free grammar, is introduced. The static Watson-Crick Context-Free grammar differs from a dynamic Watson-Crick Context-Free grammar in generating double-stranded strings, as well as for regular and linear Grammars. The main result of the paper is to determine the generative powers of static Watson-Crick Context-Free Grammars. Besides, the relationship of the families of languages generated by Chomsky Grammars, sticker systems and Watson-Crick Grammars are presented in terms of their hierarchy.

  • Watson–Crick Context-Free Grammars: Grammar Simplifications and a Parsing Algorithm
    The Computer Journal, 2018
    Co-Authors: Nurul Liyana Mohamad Zulkufli, Sherzod Turaev, Mohd Izzuddin Mohd Tamrin, Azeddine Messikh
    Abstract:

    A Watson–Crick (WK) Context-Free grammar, a Context-Free grammar with productions whose right-hand sides contain nonterminals and double-stranded terminal strings, generates complete double-stranded strings under Watson–Crick complementarity. In this paper, we investigate the simplification processes of Watson–Crick Context-Free Grammars, which lead to defining Chomsky like normal form for Watson–Crick Context-Free Grammars. The main result of the paper is a modified CYK (Cocke–Younger–Kasami) algorithm for Watson–Crick Context-Free Grammars in WK-Chomsky normal form, allowing to parse double-stranded strings in O(n^6) time.

Sakakibarayasubumi - One of the best experts on this subject based on the ideXlab platform.