Browsing by Subject "Context free grammars"

Now showing 1 - 3 of 3

Open Access
An ontology-based approach to parsing Turkish sentences
(Springer, 1998-10) Temizsoy, Murat; Çiçekli, ilyas
The main problem with natural language analysis is the ambiguity found in various levels of linguistic information. Syntactic analysis with word senses is frequently not enough to resolve all ambiguities found in a sentence. Although natural languages are highly connected to the real world knowledge, most of the parsing architectures do not make use of it effectively In this paper, a new methodology is proposed for analyzing Turkish sentences which is heavily based on the constraints in the ontology. The methodology also makes use of morphological marks of Turkish which generally denote semantic properties. Analysis aims to find the propositional structure of the input utterance without constructing a deep syntactic tree, instead it utilizes a weak interaction between syntax and semantics. The architecture constructs a specific meaning representation on top of the analyzed propositional structure. © Springer-Verlag Berlin Heidelberg 1998.
Open Access
SIMARD: a simulated annealing based RNA design algorithm with quality pre-selection strategies
(IEEE, 2017-12) Sav, Sinem; Hampson, D. J. D.; Tsang, H. H.
Most of the biological processes including expression levels of genes and translation of DNA to produce proteins within cells depend on RNA sequences, and the structure of the RNA plays vital role for its function. RNA design problem refers to the design of an RNA sequence that folds into given secondary structure. However, vast number of possible nucleotide combinations make this an NP-Hard problem. To solve the RNA design problem, a number of researchers have tried to implement algorithms using local stochastic search, context-free grammars, global sampling or evolutionary programming approaches. In this paper, we examine SIMARD, an RNA design algorithm that implements simulated annealing techniques. We also propose QPS, a mutation operator for SIMARD that pre-selects high quality sequences. Furthermore, we present experiment results of SIMARD compared to eight other RNA design algorithms using the Rfam datset. The experiment results indicate that SIMARD shows promising results in terms of Hamming distance between designed sequence and the target structure, and outperforms ERD in terms of free energy. © 2016 IEEE.
Open Access
Towards heuristic algorithmic memory
(Springer, Berlin, Heidelberg, 2011) Özkural, Eray
We propose a long-term memory design for artificial general intelligence based on Solomonoff's incremental machine learning methods. We introduce four synergistic update algorithms that use a Stochastic Context-Free Grammar as a guiding probability distribution of programs. The update algorithms accomplish adjusting production probabilities, re-using previous solutions, learning programming idioms and discovery of frequent subprograms. A controlled experiment with a long training sequence shows that our incremental learning approach is effective. © 2011 Springer-Verlag Berlin Heidelberg.