Boosting the Efficiency of First-Order Abductive Reasoning Using Pre-estimated Relatedness between Predicates
Boosting the Efficiency of First-Order Abductive Reasoning Using Pre-estimated Relatedness between Predicates
复制标题
DOI：
10.7763/ijmlc.2015.v5.493
复制
发表时间：
2015-04
期刊：
影响因子：
--
通讯作者：
Kazeto Yamamoto;Naoya Inoue;Kentaro Inui;Yuki Arase;Junichi Tsujii
中科院分区：
文献类型：
--
作者：
Kazeto Yamamoto;Naoya Inoue;Kentaro Inui;Yuki Arase;Junichi Tsujii
诱拐是对最佳解释的推论。虽然溯因长期以来一直被认为是一种很有前途的自然语言处理框架，但其计算复杂性阻碍了其在实际自然语言处理问题中的应用。在本文中，我们提出了一种方法来预先确定谓词之间的语义关系，并利用这些信息来提高一阶溯因推理的效率。该方法使用估计的语义相关度如下：(I)阻止导致语义上与观测无关的解释的推理，以及(Ii)对语义上相关的观测进行聚类，以便将推理任务划分为一组可以并行解决的不相互依赖的子问题。在大规模知识库上进行的实验表明，与现有的一阶溯因推理系统相比，该方法大大减少了搜索空间，显著提高了一阶溯因推理的计算效率。
Abduction is inference to the best explanation. While abduction has long been considered a promising framework for natural language processing (NLP), its computational complexity hinders its application to practical NLP problems. In this paper, we propose a method to predetermine the semantic relatedness between predicates and to use that information to boost the efficiency of first-order abductive reasoning. The proposed method uses the estimated semantic relatedness as follows: (i) to block inferences leading to explanations that are semantically irrelevant to the observations, and (ii) to cluster semantically relevant observations in order to split the task of abduction into a set of non-interdependent subproblems that can be solved in parallel. Our experiment with a large-scale knowledge base for a real-life NLP task reveals that the proposed method drastically reduces the size of the search space and significantly improves the computational efficiency of first-order abductive reasoning compared with the state-of-the-art system.