In this paper we explore a task-driven approach to interfacing NLP components, where language processing is guided by the end-task that each application requires. The core idea is to generalize feature values into feature value distributions, representing under-specified feature values, and to fit linguistic pipelines with a back-channel of specification requests through which subsequent components can declare to preceding ones the importance of narrowing the value distribution of particular features that are critical for the current task.

The intuition and basic hypothesis that this paper explores is that names are more characteristic of their language than common words are, and that a single name can have enough clues to confidently identify its language where random text of the same length wouldn't. To test this hypothesis, n-gramm modelling is used to learn language models which identify the language of isolated names and equally short document fragments. As the empirical results corroborate the prior intuition, an explanation is sought for the higher accuracy at which the language of names can be identified. The results of the application of these models, as well as the models themselves, are quantitatively and qualitatively analysed and a hypothesis is formed about the explanation of this difference. The conclusions derived are both technologically useful in information extraction or text-to-speech tasks, and theoretically interesting as a tool for improving our understanding of the morphology and phonology of the languages involved in the experiments.