判别训练有限状态的条件随机场 字符串编辑距离

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摘要翻译：
在信息抽取、对象识别、数据挖掘、生物序列分析等领域都需要度量序列相似性。本文提出了判别型串编辑CRFs，这是一种用于串间编辑序列的有限条件随机场模型。条件随机场比生成方法（如对HMM或Ristad和Yianilos的工作）有优势，因为作为条件训练的方法，它们能够使用复杂的、任意的动作和输入字符串的特征。与生成模型一样，训练数据不必指定给定字符串对之间的编辑序列。然而，与生成模型不同的是，我们的模型是在字符串对的正和负实例上训练的。我们在几个数据集上给出了积极的实验结果。
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英文标题：
《A Conditional Random Field for Discriminatively-trained Finite-state
  String Edit Distance》
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作者：
Andrew McCallum, Kedar Bellare, Fernando Pereira
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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：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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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：
  The need to measure sequence similarity arises in information extraction, object identity, data mining, biological sequence analysis, and other domains. This paper presents discriminative string-edit CRFs, a finitestate conditional random field model for edit sequences between strings. Conditional random fields have advantages over generative approaches to this problem, such as pair HMMs or the work of Ristad and Yianilos, because as conditionally-trained methods, they enable the use of complex, arbitrary actions and features of the input strings. As in generative models, the training data does not have to specify the edit sequences between the given string pairs. Unlike generative models, however, our model is trained on both positive and negative instances of string pairs. We present positive experimental results on several data sets.
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PDF链接：
https://arxiv.org/pdf/1207.1406

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关键词：字符串 Presentation Applications Experimental Intelligence 数据挖掘 模型 random string sequence