POS Tagging without a Tagger: Using Aligned Corpora for Transferring Knowledge to Under-Resourced Languages

Authors

  • Ines Turki Khemakhem MIRACL Laboratory, University of Sfax
  • Salma Jamoussi MIRACL Laboratory, University of Sfax
  • Abdelmajid Ben Hamadou MIRACL Laboratory, University of Sfax

DOI:

https://doi.org/10.13053/cys-20-4-2430

Keywords:

POS tagging, alignment, parallel corpus, under-resourced languages.

Abstract

Almost all languages lack sufficient resources and tools for developing Human Language Technologies (HLT). These latters are mostly concerned by languages for which large resources and tools are available.  In this paper, we will prove that under-resourced languages can benefit from these available resources and tools to develop their own HLT by taking as an example the which of the POS tagging Task that is among the most primordial Natural Language Processing tasks. Since, it assigns word tag to highlight its syntactic features by considering the corresponding contexts. The solution that we propose, in this research work, is based on the use of aligned parallel corpus as a bridge between a rich-resourced language and an under resourced language. This kind of corpus is usually available. The rich language side of this corpus is first annotated. These POS-annotations were then exploited to predict the annotation of under-resourced language side by using alignment training. After this training step, we obtain a matching table between the two languages which will be exploited to annotate an input text.  The experimentation of the proposed approach is performed on a couple of languages: English as a rich language and Arabic as an under resourced language. We used the IWSLT10 training corpus, and English Treetagger. The approach was evaluated on the test corpus extracted from the IWSLT08 obtain a F-score of 89% and can be extrapolated to the other NLP tasks.

Author Biographies

Ines Turki Khemakhem, MIRACL Laboratory, University of Sfax

Researcher and assistant at Sfax University. She received her Ph.D. in computer Science in 2016. She iscurrently a research member in the MIRACL (Multimedia Information system and Advanced Computing Laboratory) laboratory. Her Ph.D. work aims to integrate morpho-syntactic and semantic information for statistical machine translation. Her main interest focuses on Arabic language processing.

Salma Jamoussi, MIRACL Laboratory, University of Sfax

Researcher and assistant professor at Sfax University in the higher institute of computer science and multimedia. She received her Ph.D. in computer Science in 2004 from the Henri Poincaré University, France. She focuses her research on classification methods, datamining and natural language processing.

Abdelmajid Ben Hamadou, MIRACL Laboratory, University of Sfax

Obtained a doctorate degree in computer science from the University of Orsay (France) in November 1979 and a These d′Etat in Computer Science from the University of Tunis (Tunisia) in March 1993. He is presently Professor of Computer Science at the Higher Institute of Computer science and Multimedia, Sfax-university and member of the Research Laboratory MIRACL at the same institution. In July 2002 he was decorated by the President of the Tunisian Republic (“Merit in Education and Science”) and in May 2009, he received from the Vice President of Syria the "Al-Kindi" Award (“the best computer science researcher”). Abdelmajid Ben Hamadou has published more than 280 articles in journals and conferences and has supervized more than 40 doctoral theses. His research domains are: Natural Language Processing, semantic web, information retrieval/filtering and document summarizing.

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Published

2016-12-18

Issue

Vol. 20 No. 4 (2016): Thematic Issue: Research Advances and Applications of Evolutionary Computation (Guest Editors: H.J. Escalante, M. Graff)

Section

Articles

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