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/library/oar/handle/123456789/130469| Title: | Cross-lingual link discovery for under-resourced languages |
| Authors: | Rosner, Michael Ahmadi, Sina Apostol, Elena-Simona Bosque-Gil, Julia Chiarcos, Christian Dojchinovski, Milan Gkirtzou, Katerina Gracia, Jorge Gromann, Dagmar Liebeskind, Chaya Oleskeviciene, Giedre Valunaite Serasset, Gilles Truica, Ciprian-Octavian |
| Keywords: | Linked data Natural language processing (Computer science) Semantic computing Artificial intelligence |
| Issue Date: | 2022 |
| Citation: | Rosner, M., Ahmadi, S., Apostol, E. S., Bosque-Gil, J., Chiarcos, C., Dojchinovski, M., ... & Truica, C. O. (2022). Cross-lingual link discovery for under-resourced languages. 13th International Conference on Language Resources and Evaluation (LREC), Marseille, France. 181-192. |
| Abstract: | In this paper, we provide an overview of current technologies for cross-lingual link discovery, and we discuss challenges, experiences and prospects of their application to under-resourced languages. We first introduce the goals of cross-lingual linking and associated technologies, and in particular, the role that the Linked Data paradigm (Bizer et al., 2011) applied to language data can play in this context. We define under-resourced languages with a specific focus on languages actively used on the internet, i.e., languages with a digitally versatile speaker community, but limited support in terms of language technology. We argue that languages for which considerable amounts of textual data and (at least) a bilingual word list are available, techniques for cross-lingual linking can be readily applied, and that these enable the implementation of downstream applications for under-resourced languages via the localisation and adaptation of existing technologies and resources. |
| URI: | https://www.um.edu.mt/library/oar/handle/123456789/130469 |
| Appears in Collections: | Scholarly Works - FacICTAI |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Cross_lingual_link_discovery_for_under_resourced_languages_2022.pdf | 295.31 kB | Adobe PDF | View/Open |
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