Abstract. In this work we investigate several issues in order to improve the performance of probabilistic estimation trees (PETs). First, we derive a new probability smoothing that...
The majority of the existing algorithms for learning decision trees are greedy--a tree is induced top-down, making locally optimal decisions at each node. In most cases, however, ...
We study unsupervised methods for learning refinements of the nonterminals in a treebank. Following Matsuzaki et al. (2005) and Prescher (2005), we may for example split NP withou...
This paper proposes a novel method to refine the grammars in parsing by utilizing semantic knowledge from HowNet. Based on the hierarchical state-split approach, which can refine ...
Xiaojun Lin, Yang Fan, Meng Zhang, Xihong Wu, Huis...
Abstract— In this paper we propose MARAS, a biologicallyinspired method for routing in a mobile ad-hoc/sensor network environment. We assume that all nodes have no explicit knowl...