Unsupervised and Lightly Supervised Part-of-Speech Tagging Using Recurrent Neural Networks
Othman Zennaki, Nasredine Semmar, Laurent Besacier · Institutional Repositories DataBase (IRDB) · 2015
In this paper, we propose a novel approach to induce automatically a Part-Of-Speech (POS) tagger for resource-poor languages (languages that have no labeled training data).This approach is based on cross-language projection of linguistic annotations from parallel corpora without the use of word alignment information.Our approach does not assume any knowledge about foreign languages, making it applicable to a wide range of resource-poor languages.We use Recurrent Neural Networks (RNNs) as multilingual analysis tool.Our approach combined with a basic crosslingual projection method (using word alignment information) achieves comparable results to the state-of-the-art.We also use our approach in a weakly supervised context, and it shows an excellent potential for very lowresource settings (less than 1k training utterances).