2026-04-13T15:11:32Zhttps://recercat.cat/oai/requestoai:recercat.cat:10230/276902025-12-20T16:49:13Zcom_2072_6col_2072_452952
RECERCAT
author
Ballesteros, Miguel
author
Bohnet, Bernd
author
Mille, Simon
author
Wanner, Leo
2016-12-05T08:36:08Z2016-12-05T08:36:08Z2015
Comunicació presentada a la 2015 Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL HLT 2015), celebrada del 31 de maig al 5 de juny 2015 a Denver (CO, EUA).Abstract structures from which the generation naturally starts often do not contain any functional nodes, while surface-syntactic structures or a chain of tokens in a linearized tree contain all of them. Therefore, data-driven linguistic generation needs to be able to cope with the projection between non-isomorphic structures that differ in their topology and number of nodes. So far, such a projection has been a challenge in data-driven generation/nand was largely avoided. We present a fully stochastic generator that is able to cope with projection between non-isomorphic structures. The generator, which starts from PropBank-like structures, consists of a cascade/nof SVM-classifier based submodules that map in a series of transitions the input structures onto sentences. The generator has been evaluated for English on the Penn-Treebank and for Spanish on the multi-layered AncoraUPF corpus.Our work on deep stochastic sentence generation is partially supported by the European Commission under the contract numbers FP7-ICT-610411 (project MULTISENSOR) and H2020-RIA-645012 (project KRISTINA).
© 2015 Association for Computational Linguistics info:eu-repo/semantics/openAccess
Tractament del llenguatge natural (Informàtica)
Lingüística computacional
Data-driven sentence generation with non-isomorphic trees
info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion