USING GRAPHEMEn-GRAMS IN SPELLING CORRECTION AND AUGMENTATIVE TYPING SYSTEMS

Alket Memushaj and Tarek M. Sobh ()
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Alket Memushaj: School of Engineering, University of Bridgeport, Connecticut, USA
Tarek M. Sobh: School of Engineering, University of Bridgeport, Connecticut, USA

New Mathematics and Natural Computation (NMNC), 2008, vol. 04, issue 01, 87-106

Abstract: Probabilistic language models have gained popularity in Natural Language Processing due to their ability to successfully capture language structures and constraints with computational efficiency. Probabilistic language models are flexible and easily adapted to language changes over time as well as to some new languages. Probabilistic language models can be trained and their accuracy strongly related to the availability of large text corpora.In this paper, we investigate the usability of grapheme probabilistic models, specifically graphemen-grams models in spellchecking as well as augmentative typing systems. Graphemen-gram models require substantially smaller training corpora and that is one of the main drivers for this thesis in which we build graphemen-gram language models for the Albanian language. There are presently no available Albanian language corpora to be used for probabilistic language modeling.Our technique attempts to augment spellchecking and typing systems by utilizing graphemen-gram language models in improving suggestion accuracy in spellchecking and augmentative typing systems. Our technique can be implemented in a standalone tool or incorporated in another tool to offer additional selection/scoring criteria.

Keywords: Natural language processing; language modeling; statistical language modeling; graphemen-grams (search for similar items in EconPapers)
Date: 2008
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DOI: 10.1142/S1793005708000970

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