A Note of Zipf's Law, Natural Languages, and Noncoding DNA Regions

In Phys. Rev. Letters (73:2), Mantegna et al. conclude on the basis of Zipf rank frequency data that noncoding DNA sequence regions are more like natural languages than coding regions. We argue on the contrary that an empirical fit to Zipf"s "law" cannot be used as a criterion for similarity to natural languages. Although DNA is a presumably "organized system of signs" in Mandelbrot"s (1961) sense, and observation of statistical featurs of the sort presented in the Mantegna et al. paper does not shed light on the similarity between DNA's "gramar" and natural language grammars, just as the observation of exact Zipf-like behavior cannot distinguish between the underlying processes of tossing an M-sided die or a finite-state branching process.

Computational Consequences of Agreement and Ambiguity in Natural Language

The computer science technique of computational complexity analysis can provide powerful insights into the algorithm-neutral analysis of information processing tasks. Here we show that a simple, theory-neutral linguistic model of syntactic agreement and ambiguity demonstrates that natural language parsing may be computationally intractable. Significantly, we show that it may be syntactic features rather than rules that can cause this difficulty. Informally, human languages and the computationally intractable Satisfiability (SAT) problem share two costly computional mechanisms: both enforce agreement among symbols across unbounded distances (Subject-Verb agreement) and both allow ambiguity (is a word a Noun or a Verb?).