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摘要翻译：
本文介绍并分析了语义角色标注问题的一组推理模型：一个基于约束满足的推理模型，以及几种利用判别分类器将推理建模为元学习问题的策略。这些分类器具有丰富的编码命题和句子级信息的新特征。就我们所知，这是第一个：(a)对语义角色标注的基于学习的推理模型进行深入分析，以及(b)在此背景下对几种推理策略进行比较的工作。我们在CoNLL-2005共享任务框架下，仅使用自动生成的句法信息来评估所提出的推理策略。大量的实验评估和分析表明，所有提出的推理策略都是成功的--它们都超过了CoNLL-2005评估练习中报告的当前最佳结果--但每一种提出的方法都有其优缺点。从这一分析中可以看出最先进的SRL组合策略的几个重要特征：(i)单个模型应该在候选参数的粒度上组合，而不是在完整解的粒度上组合；(ii)最佳组合策略使用基于学习的推理模型；(iii)基于学习的推理得益于最大裕度分类器和全局反馈。
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英文标题：
《Combination Strategies for Semantic Role Labeling》
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作者：
M. Surdeanu, L. Marquez, X. Carreras, P. R. Comas
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最新提交年份：
2011
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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：
This paper introduces and analyzes a battery of inference models for the problem of semantic role labeling: one based on constraint satisfaction, and several strategies that model the inference as a meta-learning problem using discriminative classifiers. These classifiers are developed with a rich set of novel features that encode proposition and sentence-level information. To our knowledge, this is the first work that: (a) performs a thorough analysis of learning-based inference models for semantic role labeling, and (b) compares several inference strategies in this context. We evaluate the proposed inference strategies in the framework of the CoNLL-2005 shared task using only automatically-generated syntactic information. The extensive experimental evaluation and analysis indicates that all the proposed inference strategies are successful -they all outperform the current best results reported in the CoNLL-2005 evaluation exercise- but each of the proposed approaches has its advantages and disadvantages. Several important traits of a state-of-the-art SRL combination strategy emerge from this analysis: (i) individual models should be combined at the granularity of candidate arguments rather than at the granularity of complete solutions; (ii) the best combination strategy uses an inference model based in learning; and (iii) the learning-based inference benefits from max-margin classifiers and global feedback.
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PDF链接：
https://arxiv.org/pdf/1110.0029
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