Information Theory of Random Composition Trees
Random composition trees model the hierarchical semantic structures of meaningful texts as a statistical ensemble generated by recursively splitting texts into coherent chunks. A recent study shows that different text types can be described by random composition trees with distinct branching factors, and that the entropy rates of these trees exhibit a similar dependence across text types as the entropy rates of the actual texts. Motivated by this correspondence, we analyze information-theoretic properties of this stochastic splitting model. First, we demonstrate that the entropy of random composition trees grows linearly with the root size, and we derive an analytical expression for the e...
| Authors | Doron Sivan, Weishun Zhong, Tankut Can et al. |
|---|---|
| Primary category | cs.IT |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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