Randomised techniques allow very big language models to be represented succinctly. However, being batch-based they are unsuitable for modelling an unbounded stream of language whi...
We present Tefkat, an implementation of a language designed specically for the transformation of MOF models using patterns and rules. The language adopts a declarative paradigm, w...
In this paper we propose a novel statistical language model to capture long-range semantic dependencies. Specifically, we apply the concept of semantic composition to the problem ...
In this paper we investigate random forest based language model adaptation. Large amounts of out-of-domain data are used to grow the decision trees while very small amounts of in-...
In this work we present the Subsequence Similarity Language Model (S2-LM) which is a new approach to language modeling based on string similarity. As a language model, S2-LM gener...