Unsupervised and Lightly Supervised Part-of-Speech Tagging Using Recurrent Neural NetworksReport as inadecuate




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* Corresponding author 1 LIST - Laboratoire d-Intégration des Systèmes et des Technologies 2 GETALP - Groupe d’Étude en Traduction Automatique-Traitement Automatisé des Langues et de la Parole LIG - Laboratoire d-Informatique de Grenoble 3 IUF - Institut Universitaire de France

Abstract : 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 cross-lingual 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 low-resource settings less than 1k training utterances.





Author: Othman Zennaki - Nasredine Semmar - Laurent Besacier -

Source: https://hal.archives-ouvertes.fr/



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