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Using machine learning for translation inference across dictionaries

  • This paper describes our contribution to the closed track of the Shared Task Translation Inference across Dictionaries (TIAD2017), 1 held in conjunction with the first Conference on Language Data and Knowledge (LDK-2017). In our approach, we use supervised machine learning to predict high-quality candidate translation pairs. We train a Support Vector Machine using several features, mostly of the translation graph, but also taking into consideration string similarity (Levenshtein distance). As the closed track does not provide manual training data, we define positive training examples as translation candidate pairs which occur in a cycle in which there is a direct connection.

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Metadaten
Author:Kathrin Donandt, Christian ChiarcosORCiDGND, Maxim Ionov
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/104113
URL:https://ceur-ws.org/Vol-1899/
ISSN:1613-0073OPAC
Parent Title (English):LDK Workshops 2017 - LDK Workshops: OntoLex, TIAD and Challenges for Wordnets: Proceedings of the LDK 2017 Workshops: 1st Workshop on the OntoLex Model (OntoLex-2017), Shared Task on Translation Inference Across Dictionaries & Challenges for Wordnets co-located with 1st Conference on Language, Data and Knowledge (LDK 2017), Galway, Ireland, June 18, 2017
Publisher:CEUR-WS
Place of publication:Aachen
Editor:John P. McCrae, Francis Bond, Paul Buitelaar, Philipp Cimiano, Thierry Declerck, Jorge Gracia, Ilan Kernerman, Elena Montiel Ponsoda, Noam Ordan, Maciej Piasecki
Type:Conference Proceeding
Language:English
Date of Publication (online):2023/04/27
Year of first Publication:2017
Publishing Institution:Universität Augsburg
Release Date:2023/05/16
First Page:103
Last Page:112
Series:CEUR Workshop Proceedings ; 1899
Institutes:Philologisch-Historische Fakultät
Philologisch-Historische Fakultät / Angewandte Computerlinguistik
Philologisch-Historische Fakultät / Angewandte Computerlinguistik / Lehrstuhl für Angewandte Computerlinguistik (ACoLi)

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