摘要翻译：
本文提出了一种用于统计关系学习的最大后验概率(MAP)推理方法&切割平面推理(CPI)。它以马尔可夫逻辑为框架，受切割平面法的启发，可以看作是一种元算法，它实例化大型复杂马尔可夫网络的小部分，然后用传统的映射方法求解这些部分。我们在两个任务上评估CPI，语义角色标记和联合实体分解，同时插入两种不同的映射推理方法：马尔可夫逻辑中映射推理的当前选择方法MaxWalkSAT和整数线性规划。我们观察到，当与CPI一起使用时，这两种方法都比单独使用时要快得多。此外，CPI提高了MaxWalkSAT的精度，保持了整数线性规划的正确性。
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英文标题：
《Improving the Accuracy and Efficiency of MAP Inference for Markov Logic》
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作者：
Sebastian Riedel
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  In this work we present Cutting Plane Inference (CPI), a Maximum A Posteriori (MAP) inference method for Statistical Relational Learning. Framed in terms of Markov Logic and inspired by the Cutting Plane Method, it can be seen as a meta algorithm that instantiates small parts of a large and complex Markov Network and then solves these using a conventional MAP method. We evaluate CPI on two tasks, Semantic Role Labelling and Joint Entity Resolution, while plugging in two different MAP inference methods: the current method of choice for MAP inference in Markov Logic, MaxWalkSAT, and Integer Linear Programming. We observe that when used with CPI both methods are significantly faster than when used alone. In addition, CPI improves the accuracy of MaxWalkSAT and maintains the exactness of Integer Linear Programming.
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PDF链接：
https://arxiv.org/pdf/1206.3282