An efficient context-free parsing algorithm
Communications of the ACM
Transition network grammars for natural language analysis
Communications of the ACM
Statistical models for unsupervised prepositional phrase attachment
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Towards a database for genotype-phenotype association research: mining data from encyclopaedia
International Journal of Data Mining and Bioinformatics
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We present here filtered-popping recursive transition networks (FPRTNs), a special breed of RTNs, and an efficient parsing algorithm based on recursive transition network with string output (RTNSO) which constructs the set of parses of a potentially ambiguous sentence as a FPRTN in polynomial time. By constructing a FPRTN rather than a parse enumeration, we avoid the exponential explosion due to cases where the number of parses increases exponentially w.r.t. the input length. The algorithm is compatible with the grammars that can be manually developed with the Intex and Unitex systems.