Othman Zennaki - Automatic creation of linguistic tools and resources from parallel corpora

14:00
Monday
11
Mar
2019
Speaker: 
Othman Zennaki
Teams: 
Keywords: 
Detailed information: 

 

Cette soutenance aura lieu Lundi 11 Mars 2019 à 14h00
Adresse de la soutenance : CEA LIST - Site NANO INNOV, Avenue de la Vauve, Bâtiment 862, Amphithéâtre 33, 91120 Palaiseau 

Jury :

  • Laurent  Besacier, professeur, Universite Grenoble Alpes, directeur de thèse
  • Reinhard Rapp, professeur, Johannes Gutenberg-Universität Mainz, rapporteur
  • Mounir  Zrigui, professeur, Universite de Monastir - Tunisie, rapporteur
  • Nasredine  Semmar, ingenieur de recherche, CEA List, examinateur
  • Sophie  Rosset, directrice de recherche,  LIMSI CNRS, examinateur
Abstract: 

This thesis focuses on the automatic construction of linguistic tools and resources for analyzing texts of low-resource languages. We propose an approach using Recurrent Neural Networks (RNN) and requiring only a parallel or multi-parallel corpus between a well-resourced language and one or more low-resource languages. This parallel or multi-parallel corpus is used to construct a multilingual representation of words of the source and target languages. We used this multilingual representation to train our neural models and we investigated both uni and bidirectional RNN models. We also proposed a method to include external information (for instance, low-level information from Part-Of-Speech tags) in the RNN to train higher level taggers (for instance, SuperSenses taggers and Syntactic dependency parsers). We demonstrated the validity and genericity of our approach on several languages and we conducted experiments on various NLP tasks: Part-Of-Speech tagging, SuperSenses tagging and Dependency parsing. The obtained results are very satisfactory. Our approach has the following characteristics and advantages: (a) it does not use word alignment information, (b) it does not assume any knowledge about target languages (one requirement is that the two languages (source and target) are not too syntactically divergent), which makes it applicable to a wide range of low-resource languages, (c) it provides authentic multilingual taggers (one tagger for N languages).