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EM-training for probabilistic aligned hypergraph bimorphisms
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Foundations of Language Processing)ORCID iD: 0000-0001-7349-7693
Technische Universität Dresden.
Technische Universität Dresden.
2016 (English)In: Proc. StatFSM 2016: ACL Workshop on statistical NLP and weighted automata, The Association for Computational Linguistics , 2016Conference paper, Published paper (Refereed)
Abstract [en]

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.
Keyword [en]
EM training, bimorphism, hyperedge replacement, maximum likelihood
National Category
Computer Science Language Technology (Computational Linguistics)
Research subject
Computer Science; datorlingvistik
Identifiers
URN: urn:nbn:se:umu:diva-121676OAI: oai:DiVA.org:umu-121676DiVA: diva2:933575
Conference
StatFSM 2016: ACL Workshop on statistical NLP and weighted automata
Available from: 2016-06-06 Created: 2016-06-06 Last updated: 2016-06-06

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Language
  • de-DE
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  • nn-NB
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Output format
  • html
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