A language modeling based on a hierarchical approach: m_n^v

In this work, we introduce the concept of hierarchical M n language model and we compare it to the based class multigram and interpolated class n-gram model. The originality of our approach is its capability to parse a string of class/tags into variable length dependent sequences. A few experimental tests were carried out on a class corpus extracted from the French “Le Monde” word corpus labeled automatically. In our experiments, M n outperforms based class multigram and interpolated class bigram but are comparable to the interpolated class trigram model.