Token merging in language model-based confusible disambiguation

Stehouwer, H., & Van Zaanen, M. (2009). Token merging in language model-based confusible disambiguation. In T. Calders, K. Tuyls, & M. Pechenizkiy (Eds.), Proceedings of the 21st Benelux Conference on Artificial Intelligence (pp. 241-248).
In the context of confusible disambiguation (spelling correction that requires context), the synchronous back-off strategy combined with traditional n-gram language models performs well. However, when alternatives consist of a different number of tokens, this classification technique cannot be applied directly, because the computation of the probabilities is skewed. Previous work already showed that probabilities based on different order n-grams should not be compared directly. In this article, we propose new probability metrics in which the size of the n is varied according to the number of tokens of the confusible alternative. This requires access to n-grams of variable length. Results show that the synchronous back-off method is extremely robust. We discuss the use of suffix trees as a technique to store variable length n-gram information efficiently.
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