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EM-training for probabilistic aligned hypergraph bimorphisms
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (Foundations of Language Processing)ORCID-id: 0000-0001-7349-7693
Technische Universität Dresden.
Technische Universität Dresden.
2016 (engelsk)Inngår i: Proceedings of the SIGFSM Workshop on Statistical NLP and Weighted Automata, Association for Computational Linguistics , 2016, s. 60-69Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Association for Computational Linguistics , 2016. s. 60-69
Emneord [en]
EM training, bimorphism, hyperedge replacement, maximum likelihood
HSV kategori
Forskningsprogram
datalogi; datorlingvistik
Identifikatorer
URN: urn:nbn:se:umu:diva-121676DOI: 10.18653/v1/W16-2407OAI: oai:DiVA.org:umu-121676DiVA, id: diva2:933575
Konferanse
Workshop Statistical Natural Language Processing and Weighted Automata, Berlin, Germany, August 12, 2016
Tilgjengelig fra: 2016-06-06 Laget: 2016-06-06 Sist oppdatert: 2019-06-26bibliografisk kontrollert

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