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Conference papers

Cleaning statistical language models

Reda Jourani 1 David Langlois 2 Kamel Smaïli 2 Khalid Daoudi 1 Driss Aboutajdine 3
1 GeoStat - Geometry and Statistics in acquisition data
Inria Bordeaux - Sud-Ouest
2 PAROLE - Analysis, perception and recognition of speech
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
3 GSCM-LRIT - Laboratoire de Recherche en Informatique et Télécommunications [Rabat]
Abstract : In this paper, we describe how to decide a n-gram is actually impossible in a language. We use decision rules on a corpus tagged with POS. These rules are based on statistics and phonological criteria. In terms of statistical language modeling, deciding that a n-gram is impossible leads to assign to it a null probability.We defer on the possible n-grams the released mass of probabilities. To do this, we define a new formulation of P(w|h). We apply the principle of impossible events to bigrams. Then we use the list of impossible bigrams to build a list of impossible trigrams. The new trigram model exceeds the baseline model by 5.53% in terms of perplexity.
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Conference papers
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https://hal.inria.fr/inria-00579376
Contributor : David Langlois Connect in order to contact the contributor
Submitted on : Wednesday, March 23, 2011 - 4:06:30 PM
Last modification on : Friday, February 26, 2021 - 3:28:06 PM

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  • HAL Id : inria-00579376, version 1

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CNRS | INRIA | LORIA | UNIV-LORRAINE

Citation

Reda Jourani, David Langlois, Kamel Smaïli, Khalid Daoudi, Driss Aboutajdine. Cleaning statistical language models. 3d. International Conference on Information Systems and Economic Intelligence (SIIE'2010), Feb 2010, Sousse, Tunisia. ⟨inria-00579376⟩

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