939 research outputs found
MultiFarm: A benchmark for multilingual ontology matching
In this paper we present the MultiFarm dataset, which has been designed as a benchmark for multilingual
ontology matching. The MultiFarm dataset is composed of a set of ontologies translated in different
languages and the corresponding alignments between these ontologies. It is based on the OntoFarm dataset, which has been used successfully for several years in the Ontology Alignment Evaluation Initiative (OAEI). By translating the ontologies of the OntoFarm dataset into eight different languages – Chinese, Czech, Dutch, French, German, Portuguese, Russian, and Spanish – we created a comprehensive set of realistic test cases. Based on these test cases, it is possible to evaluate and compare the performance of matching approaches with a special focus on multilingualism
Dependency Parsing using Prosody Markers from a Parallel Text
Proceedings of the Ninth International Workshop
on Treebanks and Linguistic Theories.
Editors: Markus Dickinson, Kaili MĂĽĂĽrisep and Marco Passarotti.
NEALT Proceedings Series, Vol. 9 (2010), 127-138.
© 2010 The editors and contributors.
Published by
Northern European Association for Language
Technology (NEALT)
http://omilia.uio.no/nealt .
Electronically published at
Tartu University Library (Estonia)
http://hdl.handle.net/10062/15891
Korean-to-Chinese Machine Translation using Chinese Character as Pivot Clue
Korean-Chinese is a low resource language pair, but Korean and Chinese have a
lot in common in terms of vocabulary. Sino-Korean words, which can be converted
into corresponding Chinese characters, account for more than fifty of the
entire Korean vocabulary. Motivated by this, we propose a simple linguistically
motivated solution to improve the performance of the Korean-to-Chinese neural
machine translation model by using their common vocabulary. We adopt Chinese
characters as a translation pivot by converting Sino-Korean words in Korean
sentences to Chinese characters and then train the machine translation model
with the converted Korean sentences as source sentences. The experimental
results on Korean-to-Chinese translation demonstrate that the models with the
proposed method improve translation quality up to 1.5 BLEU points in comparison
to the baseline models.Comment: 9 page
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