Shared memories of bilingual subsentential alignments
type: Dataset
description: This research belongs to the Natural Language Processing (NLP) field and more specifically focuses on the topic of Sub-sentential Alignments which is closely related to Machine Translation. The originality of this work consists in an example-based approach bootstrapped by the participation of non-expert annotators through an appropriate interface. The quest for a greater expressivity, such as observed in manual alignments, mainly motivates the whole approach. An important effort has been made to define a formal environment for this original architecture based on aligned examples. Several memories have been created, using syntactic informations from parsers outputs with reasonnable low-tech requirements. Two new alignment methods were compared with state-of-theart measures and three transformational metrics were introduced
landing page: https://theses.hal.science/tel-00981005
keyword: alignement-sous-phrastique
keyword: annotation
keyword: corpus-de-reference
keyword: divergence
keyword: expressivite
keyword: expressivity
keyword: golden-corpus
keyword: human-machine-interface
keyword: infoeu-reposemanticsdoctoralthesis
keyword: infoinfo-clcomputer-science-cscomputation-and-language-cscl
keyword: interface-homme-machine
keyword: sub-sentential-alignement
keyword: theses
issued: 2026-05-05T14:00:50.690174
modified: 2026-05-05T14:00:50.690180
language: fr
contact point: Organization
  fn: CCSD

test_moissonnage_selune
type: Agent

Shared memories of bilingual subsentential alignments
type: Distribution
format: HTML
access URL: https://theses.hal.science/tel-00981005
issued: 2026-05-05T14:00:50.694282
modified: 2026-05-05T14:00:50.670782