Abin-based ontological framework for low-resourcen-gram smoothing in language modelling

In this paper, we introduce a novel method of smoothing language models (LM) based on the semantic information found in ontologies that is especially adapted for limited-resources language modeling. We exploit the latent knowledge of language that is deeply encoded within ontologies. As such, this work examines the potential of using the semantic and syntactic relations between words from the WordNet ontology to generate new plausible contexts for unseen events to simulate a larger corpus. These unseen events are then mixed-up with a baseline Witten-Bell(WB) LM in order to improve its performance both in terms of language model perplexity and automatic speech recognition word error rates. Results indicate a significant reduction in the perplexity of the language model (up to 9.85% relative) all the while reducing word error rate in a statistically significant manner compared to both the original WB LM and baseline Kneser-Ney smoothed language model on the Wall Street Journal-based Continuous Speech Recognition Phase II corpus.