An overview of the European Union’s highly multilingual parallel corpora

Steinberger, Ralf; Ebrahim, Mohamed; Poulis, Alexandros; Carrasco-Benitez, Manuel; Schlüter, Patrick; Przybyszewski, Marek; Gilbro, Signe

doi:10.1007/s10579-014-9277-0

Cited by 33 publications

(18 citation statements)

References 14 publications

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“…For a broader domain coverage of datasets necessary to train an SMT system, we merged several parallel corpora, e.g. JRC-Acquis [3], Europarl [4], DGT (translation memories generated by the Directorate-General for Translation) [5], MultiUN corpus [6] and TED talks [7] among others, into one parallel dataset. For the translation approach, we engage the widely used Moses toolkit [8].…”

Section: Statistical Machine Translationmentioning

confidence: 99%

Translating Ontologies in Real-World Settings

Arčan

Dragoni

Buitelaar

2016

Lecture Notes in Computer Science

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To enable knowledge access across languages, ontologies that are often represented only in English, need to be translated into different languages. The main challenge in translating ontologies is to disambiguate an ontology label with respect to the domain modelled by ontology itself. Machine translation services may help in this task; however, a crucial requirement is to have translations validated by experts before the ontologies are deployed. For this reason, real-world applications must implement a support system addressing this task to relieve experts in validating all translations. In this paper we present the Expert Supporting System for Ontology Translation, called ESSOT, which exploits the semantic information of the label's context for improving the quality of label translations. The system has been tested within the Organic.Lingua project by translating the ontology labels in three languages. In order to evaluate further the effectiveness of the system on handling different domains, additional ontologies were translated and evaluated. The results have been compared with translations provided by the Microsoft Translator API and the improvements demonstrate a better performance of the proposed approach for automatic ontology translation.

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Section: Statistical Machine Translationmentioning

confidence: 99%

Translating Ontologies in Real-World Settings

Arčan

Dragoni

Buitelaar

2016

Lecture Notes in Computer Science

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“…These sentences were extracted from different parallel corpora: 20 News Commentary (Bojar et al, 2013) for Spanish, the Basque Public Administration Institute translation memory 21 for Basque, the corpus from the official journal of the Catalan Goverment (Tiedemann, 2012) for Catalan, and the DGT translation memory (Steinberger et al, 2014) for Maltese. A set of 174,441 segments were randomly extracted from each of these corpora.…”

Section: Datamentioning

confidence: 99%

Assisting non-expert speakers of under-resourced languages in assigning stems and inflectional paradigms to new word entries of morphological dictionaries

Esplà-Gomis

Carrasco

Sánchez-Cartagena

et al. 2016

Lang Resources & Evaluation

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This paper presents a new method with which to assist individuals with no background in linguistics to create monolingual dictionaries such as those used by the morphological analysers of many natural language processing applications. The involvement of non-expert users is especially critical for under-resourced languages which either lack or cannot afford the recruitment of a skilled workforce. Adding a word to a morphological dictionary usually requires identifying its stem along with the inflection paradigm that can be used in order to generate all the word forms of the new entry. Our method works under the assumption that the average speakers of a language can successfully answer the polar question "is x a valid form of the word w to be inserted?", where x represents tentative alternative (inflected) forms of the new word w. The experiments show that with a small number of polar questions the correct stem and paradigm can be obtained from non-experts with high success rates. We study the impact of different heuristic and probabilistic approaches on the actual number of questions.

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“…For a broader domain coverage of an SMT system, we merged several parallel corpora necessary to train an SMT system, e.g. JRC-Acquis [13], Europarl [14], DGT (translation memories generated by the Directorate-General for Translation) [15], Mul-tiUN corpus [16] and TED talks [17] among others, into one parallel dataset. For the translation approach, the OTTO System engages the widely used Moses toolkit [18].…”

Section: Statistical Machine Translationmentioning

confidence: 99%

ESSOT: An Expert Supporting System for Ontology Translation

Arčan

Dragoni

Buitelaar

2016

Natural Language Processing and Information Systems

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To enable knowledge access across languages, ontologies, mostly represented only in English, need to be translated into different languages. The main challenge in translating ontologies with machine translation is to disambiguate an ontology label with respect to the domain modelled by the ontology itself; however, a crucial requirement is to have translations validated by experts before the ontologies are deployed. Real-world applications have to implement a support system addressing this task to help experts in validating automatically generated translations. In this paper, we present ESSOT, an Expert Supporting System for Ontology Translation. The peculiarity of this system is to exploit the semantic information of the label's context to improve the quality of label translations. The system has been tested within the Organic.Lingua project by translating the modelled ontology in three languages, whereby the results are compared with translations provided by the Microsoft Translator API. The provided results demonstrate the viability of our proposed approach.

show abstract

An overview of the European Union’s highly multilingual parallel corpora

Cited by 33 publications

References 14 publications

Translating Ontologies in Real-World Settings

Translating Ontologies in Real-World Settings

Assisting non-expert speakers of under-resourced languages in assigning stems and inflectional paradigms to new word entries of morphological dictionaries

ESSOT: An Expert Supporting System for Ontology Translation

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