Feature assembly method for extracting relations in Chinese

Feature assembly method for extracting relations in Chinese
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中文关系抽取的特征组装方法

DOI:
10.1016/j.artint.2015.07.003
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发表时间:
2015-11
影响因子:
14.4
通讯作者:
Chen, Ping
Chen, Ping
中科院分区:
计算机科学2区
文献类型:
--
作者:
Chen, Yanping;Zheng, Qinghua;Chen, Ping

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关系抽取的目标是检测自由文本中两个实体之间的关系。在一个句子中,关系实例通常包括少量的单词,这会产生稀疏的特征表示。为了更好地利用关系实例中有限的信息,分析树和组合特征被广泛用于捕获关系实例的局部依赖。然而,基于树的解析系统的性能往往会因分块或解析错误而降低。组合特征被广泛使用,但很少有研究涉及如何组合特征以实现最佳性能。因此,在这项研究中,我们提出了一个特征组装方法的关系提取。六类候选特征(中心名词、词性标记、n-gram、全词等)的原子特征和六个约束条件(单例,位置,语法等)。用于在不同的设置中联合收割机这些特征。根据候选特征的利用率,可以探索不同的约束条件,以实现最佳的提取性能。我们的方法是有效的捕获本地依赖,它减少了不准确的解析所造成的错误。我们使用ACE 2005中文和英文语料库测试了所提出的方法,它达到了最先进的性能,它是显着上级优于现有的方法。
The goal of relation extraction is to detect relations between two entities in free text. In a sentence, a relation instance usually comprises a small number of words, which yields a sparse feature representation. To make better use of limited information in a relation instance, parsing trees and combined features are employed widely to capture the local dependencies of relation instances. However, the performance of parsing tree-based systems is often degraded by chunking or parsing errors. Combined features are used widely, but few studies have addressed how features can be combined to achieve optimal performance. Thus, in this study, we propose a feature assembly method for relation extraction. Six types of candidate features (head noun, POS tag, n-gram, omni-word, etc.) are employed as atomic features and six constraint conditions (singleton, position, syntax, etc.) are used to combine these features in different settings. Depending on the utilization of candidate features, different constraint conditions can be explored to achieve the optimal extraction performance. Our method is effective for capturing local dependencies and it reduces the errors caused by inaccurate parsing. We tested the proposed method using the ACE 2005 Chinese and English corpora, and it achieved state-of-the-art performance, where it was significantly superior to existing methods.
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