10.5281/zenodo.4320725
https://zenodo.org/records/4320725
oai:zenodo.org:4320725
Bharathi Raja Chakravarthi
Bharathi Raja Chakravarthi
National University of Ireland Galway
Navaneethan Rajasekaran
Navaneethan Rajasekaran
Dublin City University
Mihael Arcan
Mihael Arcan
National University of Ireland Galway
Kevin McGuinness
Kevin McGuinness
Dublin City University
Noel E. O'Connor
Noel E. O'Connor
Dublin City University
John P. McCrae
John P. McCrae
0000-0002-7227-1331
National University of Ireland Galway
Bilingual Lexicon Induction across Orthographically-distinct Under-Resourced Dravidian Languages
Zenodo
2020
2020-12-13
eng
10.5281/zenodo.4320724
https://zenodo.org/communities/eu
https://zenodo.org/communities/pret-a-llod
Creative Commons Attribution 4.0 International
Bilingual lexicons are a vital tool for under-resourced languages and recent state-of-the-art approaches to this leverage pretrained monolingual word embeddings using supervised or semi-supervised approaches. However, these approaches require cross-lingual information such as seed dictionaries to train the model and find a linear transformation between the word embedding spaces. Especially in the case of low-resourced languages, seed dictionaries are not readily available, and as such, these methods produce extremely weak results on these languages. In this work, we focus on the Dravidian languages, namely Tamil, Telugu, Kannada, and Malayalam, which are even more challenging as they are written in unique scripts. To take advantage of orthographic information and cognates in these languages, we bring the related languages into a single script. Previous approaches have used linguistically sub-optimal measures such as the Levenshtein edit distance to detect cognates, whereby we demonstrate that the longest common sub-sequence is linguistically more sound and improves the performance of bilingual lexicon induction. We show that our approach can increase the accuracy of bilingual lexicon induction methods on these languages many times, making bilingual lexicon induction approaches feasible for such under-resourced languages.
European Commission
10.13039/501100000780
825182
Ready-to-use Multilingual Linked Language Data for Knowledge Services across Sectors