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Authors: Bartosz Ziółko 1 ; Suresh Manandhar 1 ; Richard C. Wilson 1 and Mariusz Ziółko 2

Affiliations: 1 University of York, United Kingdom ; 2 AGH University of Science and Technology, Poland

ISBN: 978-989-8111-60-9

Keyword(s): POS-tagging, language modelling, speech recognition, Polish.

Related Ontology Subjects/Areas/Topics: Applications ; Audio and Speech Processing ; Digital Signal Processing ; Multimedia ; Multimedia Signal Processing ; Pattern Recognition ; Software Engineering ; Telecommunications

Abstract: Language models are necessary for any large vocabulary speech recogniser. There are two main types of information which can be used to support modelling a language: syntactic and semantic. One of the ways to apply syntactic modelling is to use POS taggers. Morphological information can be statistically analysed to provide probability of a sequence of words using their POS tags. The results for Polish language modelling are presented.

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Paper citation in several formats:
Ziółko B.; Manandhar S.; C. Wilson R.; Ziółko M. and (2008). LANGUAGE MODEL BASED ON POS TAGGER.In Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2008) ISBN 978-989-8111-60-9, pages 177-180. DOI: 10.5220/0001931701770180

@conference{sigmap08,
author={Bartosz Ziółko and Suresh Manandhar and Richard {C. Wilson} and Mariusz Ziółko},
title={LANGUAGE MODEL BASED ON POS TAGGER},
booktitle={Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2008)},
year={2008},
pages={177-180},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001931701770180},
isbn={978-989-8111-60-9},
}

TY - CONF

JO - Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2008)
TI - LANGUAGE MODEL BASED ON POS TAGGER
SN - 978-989-8111-60-9
AU - Ziółko, B.
AU - Manandhar, S.
AU - C. Wilson, R.
AU - Ziółko, M.
PY - 2008
SP - 177
EP - 180
DO - 10.5220/0001931701770180

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