基于根加权树自动机的SubTree核计算的线性时间算法
New Linear-time Algorithm for SubTree Kernel Computation based on Root-Weighted Tree Automata
摘要：
树核作为自然语言自动学习的一种应用，已经被提出来应用于许多领域。在本文中，我们提出了一个新的线性时间算法的基础上的概念，加权树自动机的子树核计算。首先，我们介绍了一类新的加权树自动机，称为根加权树自动机，以及它们相关的形式树系列。然后，我们定义，从这个类，子树自动机表示有限树语言紧凑的计算模型。这使我们能够设计一个理论上有保证的线性时间算法，用于计算基于加权树自动机交集的子树核。该算法的核心思想是用加权树自动机方法中允许的状态等价类计算来代替以前方法中使用的DAG约简和节点排序步骤。我们的方法有三个主要优点：它是输出敏感的，它是免费的树类型（有序树与无序树）敏感，它是很好地适应任何增量树内核为基础的学习方法。最后，我们在广泛的合成树语言数据集上进行了各种比较实验，这些数据集适用于深入的算法分析。实验结果表明，该算法的性能优于现有的方法。
英文摘要：
Tree kernels have been proposed to be used in many areas as the automatic learning of natural language applications. In this paper, we propose a new linear time algorithm based on the concept of weighted tree automata for SubTree kernel computation. First, we introduce a new class of weighted tree automata, called Root-Weighted Tree Automata, and their associated formal tree series. Then we define, from this class, the SubTree automata that represent compact computational models for finite tree languages. This allows us to design a theoretically guaranteed linear-time algorithm for computing the SubTree Kernel based on weighted tree automata intersection. The key idea behind the proposed algorithm is to replace DAG reduction and nodes sorting steps used in previous approaches by states equivalence classes computation allowed in the weighted tree automata approach. Our approach has three major advantages: it is output-sensitive, it is free sensitive from the tree types (ordered trees versus unordered trees), and it is well adapted to any incremental tree kernel based learning methods. Finally, we conduct a variety of comparative experiments on a wide range of synthetic tree languages datasets adapted for a deep algorithm analysis. The obtained results show that the proposed algorithm outperforms state-of-the-art methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
