Task-Driven Linguistic Analysis based on an Underspecified Features Representation

Abstract

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.

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