Matrix multiplication via arithmetic progressions
Journal of Symbolic Computation - Special issue on computational algebraic complexity
An optimal linear-time parallel parser for tree adjoining languages
SIAM Journal on Computing
Tree-Adjoining Language Parsing in o(n^6) Time
SIAM Journal on Computing
TAL recognition in O(M(n2)) time
Journal of Computer and System Sciences
Tree adjoining grammars for RNA structure prediction
Theoretical Computer Science - Special issue: Genome informatics
A Parallel Parsing Algorithm for Natural Language using Tree Adjoining Grammar
Proceedings of the 8th International Symposium on Parallel Processing
Some computational properties of Tree Adjoining Grammars
ACL '85 Proceedings of the 23rd annual meeting on Association for Computational Linguistics
An efficient parsing algorithm for Tree Adjoining Grammars
ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
An Earley-type parsing algorithm for Tree Adjoining Grammars
ACL '88 Proceedings of the 26th annual meeting on Association for Computational Linguistics
Pseudoknot Identification through Learning TAGRNA
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
RNA Pseudoknot Folding through Inference and Identification Using TAGRNA
BICoB '09 Proceedings of the 1st International Conference on Bioinformatics and Computational Biology
Journal of Computer and System Sciences
Hi-index | 0.00 |
Formal grammars have been employed in biology to solve various important problems. In particular, grammars have been used to model and predict RNA structures. Two such grammars are Simple Linear Tree Adjoining Grammars (SLTAGs) and Extended SLTAGs (ESLTAGs). Performances of techniques that employ grammatical formalisms critically depend on the efficiency of the underlying parsing algorithms. In this paper, we present efficient algorithms for parsing SLTAGs and ESLTAGs. Our algorithm for SLTAGs parsing takes O({\rm min} \{m,n^4\} ) time and O({\rm min} \{m,n^4\} ) space, where m is the number of entries that will ever be made in the matrix M (that is normally used by TAG parsing algorithms). Our algorithm for ESLTAGs parsing takes O(n{\rm min} \{m,n^4\} ) time and O({\rm min} \{m,n^4\} ) space. We show that these algorithms perform better, in practice, than the algorithms of Uemura et al. [21].