EM-training for probabilistic aligned hypergraph bimorphisms
2016 (English)In: Proc. StatFSM 2016: ACL Workshop on statistical NLP and weighted automata, The Association for Computational Linguistics , 2016Conference paper (Refereed)
We define the concept of probabilistic aligned hypergraph bimorphism. Each such bimorphism consists of a probabilistic regular tree grammar, two hypergraph algebras in which the generated trees are interpreted, and a family of alignments between the two interpretations. It generates a set of bihypergraphs each consisting of two hypergraphs and an alignment between them; for instance, discontinuous phrase structures and non-projective dependency structures are bihypergraphs. We show an EM-training algorithm which takes a corpus of bihypergraphs and an aligned hypergraph bimorphism as input and calculates a probability assignment to the rules of the regular tree grammar such that in the limit the maximum-likelihood of the corpus is approximated.
Place, publisher, year, edition, pages
The Association for Computational Linguistics , 2016.
EM training, bimorphism, hyperedge replacement, maximum likelihood
Computer Science Language Technology (Computational Linguistics)
Research subject Computer Science; datorlingvistik
IdentifiersURN: urn:nbn:se:umu:diva-121676OAI: oai:DiVA.org:umu-121676DiVA: diva2:933575
StatFSM 2016: ACL Workshop on statistical NLP and weighted automata