Name: CALYOU
Description: A Comparable Spoken Algerian Corpus Harvested from YouTube
Provider: Multiple Institutions 
Paper Title: CALYOU: A Comparable Spoken Algerian Corpus Harvested from YouTube
  
Tasks: machine translation
Venue Name: Conference of the International Speech Communication Association
Authors: K. Abidi,M. Menacer,Kamel Smaïli
Abstract: This paper addresses the issue of comparability of comments 
   extracted from Youtube. The comments concern spoken 
   Algerian which could be either local Arabic, Modern Standard 
   Arabic or French. This diversity of expression arises a huge 
   number of problems concerning the data processing. In this 
   article, several methods of alignment will be proposed and 
   tested. The method which permits to best align is Word2Vec-based 
   approach that will be used iteratively. This recurrent 
   call of Word2Vec allows to improve significantly the results 
   of comparability. In fact, a dictionary-based approach leads 
   to a Recall of 4, while our approach allows to get a Recall of 
   33 at rank 1. Thanks to this approach, we built from Youtube 
   CALYOU, a Comparable Corpus of the spoken Algerian.