%T Using BabelNet to Improve OOV Coverage in SMT The results also demonstrate that BabelNet is a really useful tool for improving translation performance of SMT systems. Experimental results on English―Polish and English―Chinese language pairs show that domain adaptation can better utilize BabelNet knowledge and performs better than other methods. By taking advantage of the knowledge in BabelNet, three different methods ― using direct training data, domain-adaptation techniques and the BabelNet API ― are proposed in this paper to obtain translations for OOVs to improve system performance. BabelNet is a multilingual encyclopedic dictionary and a semantic network, which not only includes lexicographic and encyclopedic terms, but connects concepts and named entities in a very large network of semantic relations. This paper studies different strategies of using BabelNet to alleviate the negative impact brought about by OOVs.
Out-of-vocabulary words (OOVs) are a ubiquitous and difficult problem in statistical machine translation (SMT). Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)Įuropean Language Resources Association (ELRA) Using BabelNet to Improve OOV Coverage in SMT The results also demonstrate that BabelNet is a really useful tool for improving translation performance of SMT systems.", Publisher = "European Language Resources Association (ELRA)",Ībstract = "Out-of-vocabulary words (OOVs) are a ubiquitous and difficult problem in statistical machine translation (SMT).
#Babelnet api mods#
Cite (Informal): Using BabelNet to Improve OOV Coverage in SMT (Du et al., LREC 2016) Copy Citation: BibTeX Markdown MODS XML Endnote More options… PDF: = "Using '16)", European Language Resources Association (ELRA). In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 9–15, Portorož, Slovenia. Using BabelNet to Improve OOV Coverage in SMT. Anthology ID: L16-1002 Volume: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16) Month: May Year: 2016 Address: Portorož, Slovenia Venue: LREC SIG: Publisher: European Language Resources Association (ELRA) Note: Pages: 9–15 Language: URL: DOI: Bibkey: du-etal-2016-using Cite (ACL): Jinhua Du, Andy Way, and Andrzej Zydron. Abstract Out-of-vocabulary words (OOVs) are a ubiquitous and difficult problem in statistical machine translation (SMT).
0 kommentar(er)
