POS tagging in Amazighe using support vector machines and conditional random fields

Authors:
Mohamed Outahajala;Yassine Benajiba;Paolo Rosso;Lahbib Zenkouar
Affiliations:
Royal Institut for Amazighe Culture, Morocco and Ecole Mohammadia d'Ingénieurs, Morocco;Philips Research North America, Briacliff Manor;NLE Lab., EliRF, DSIC, Universidad Politécnica de Valencia, Spain;Ecole Mohammadia d'Ingénieurs, Morocco
Venue:
NLDB'11 Proceedings of the 16th international conference on Natural language processing and information systems
Year:
2011

Citing 3
Cited 0

Transformation-based error-driven learning and natural language processing: a case study in part-of-speech tagging

Computational Linguistics
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Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning

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Abstract

The aim of this paper is to present the first Amazighe POS tagger. Very few linguistic resources have been developed so far for Amazighe and we believe that the development of a POS tagger tool is the first step needed for automatic text processing. The used data have been manually collected and annotated. We have used state-of-art supervised machine learning approaches to build our POS-tagging models. The obtained accuracy achieved 92.58% and we have used the 10-fold technique to further validate our results.