The NLP Engine: A Universal Turing Machine for NLP

Understand

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.