It is commonly accepted that machine translation is a more complex task than part of speech tagging. But how much more complex? In this paper we make an attempt to develop a general framework and methodology for computing the informational and/or processing complexity of NLP applications and tasks. We define a universal framework akin to a Turning Machine that attempts to fit (most) NLP tasks into one paradigm. We calculate the complexities of various NLP tasks using measures of Shannon Entropy, and compare `simple' ones such as part of speech tagging to `complex' ones such as machine translation. This paper provides a first, though far from perfect, attempt to quantify NLP tasks under a uniform paradigm. We point out current deficiencies and suggest some avenues for fruitful research.
@article{arxiv.1503.00168,
title = {The NLP Engine: A Universal Turing Machine for NLP},
author = {Jiwei Li and Eduard Hovy},
journal= {arXiv preprint arXiv:1503.00168},
year = {2015}
}