Extracting Relations with Integrated Information Using Kernel Methods
Extracting Relations with Integrated Information Using Kernel Methods
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DOI：
10.3115/1219840.1219892
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发表时间：
2005-06
期刊：
影响因子：
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通讯作者：
Shubin Zhao;R. Grishman
中科院分区：
文献类型：
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作者：
Shubin Zhao;R. Grishman
实体关系检测是信息抽取的一种形式，它发现文本中实体对之间的预定义关系。本文介绍了一种关系检测方法，结合线索，从不同层次的句法处理，使用内核方法。从三个不同层次的处理信息被认为是：标记化，句子解析和深度依赖分析。每个信息源都由核函数表示。然后，复合核被开发来集成和扩展单个核，使得在一个级别上发生的处理错误可以由来自其他级别的信息来克服。我们提出了一个评估这些方法在2004年ACE关系检测任务，使用支持向量机，并表明，每个层次的句法处理有助于这项任务的有用信息。当对官方测试数据进行评估时，我们的方法产生了非常有竞争力的ACE值分数。我们还比较了不同内核的SVM和KNN。
Entity relation detection is a form of information extraction that finds predefined relations between pairs of entities in text. This paper describes a relation detection approach that combines clues from different levels of syntactic processing using kernel methods. Information from three different levels of processing is considered: tokenization, sentence parsing and deep dependency analysis. Each source of information is represented by kernel functions. Then composite kernels are developed to integrate and extend individual kernels so that processing errors occurring at one level can be overcome by information from other levels. We present an evaluation of these methods on the 2004 ACE relation detection task, using Support Vector Machines, and show that each level of syntactic processing contributes useful information for this task. When evaluated on the official test data, our approach produced very competitive ACE value scores. We also compare the SVM with KNN on different kernels.