| 摘要: |
| 关系抽取是信息抽取的一项子任务,用以识别文本中实体之间的语义关系.提出一种利用 DBN(deepbelief nets)模型进行基于特征的实体关系抽取方法,该模型是由多层无监督的 RBM(restricted Boltzmann machine)网络和一层有监督的 BP(back-propagation)网络组成的神经网络分类器. RBM 网络以确保特征向量映射达到最优,最后一层 BP 网络分类 RBM 网络的输出特征向量,从而训练实体关系分类器.在 ACE04 语料上进行的相关测试,一方面证明了字特征比词特征更适用于中文关系抽取任务;另一方面设计了 3 组不同的实验,分别使用正确的实体类别信息、通过实体类型分类器得到实体类型信息和不使用实体类型信息,用以比较实体类型信息对关系抽取效果的影响.实验结果表明,DBN 非常适用于基于高维空间特征的信息抽取任务,获得的效果比 SVM 和反向传播网络更好. |
| 关键词: DBN(deepbeliefnets) 神经网络 关系抽取 深层网络 字特征 |
| DOI：10.3724/SP.J.1001.2012.04181 |
| 分类号: |
| 基金项目:国家自然科学基金(61073130); 国家高技术研究发展计划(863)(2011AA01A207) |
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| Chinese Relation Extraction Based on Deep Belief Nets |
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CHEN Yu, ZHENG De-Quan, ZHAO Tie-Jun
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School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
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| Abstract: |
| Relation extraction is a fundamental task in information extraction, which is to identify the semanticrelationships between two entities in the text. In this paper, deep belief nets (DBN), which is a classifier of acombination of several unsupervised learning networks, named RBM (restricted Boltzmann machine) and asupervised learning network named BP (back-propagation), is presented to detect and classify the relationshipsamong Chinese name entities. The RBM layers maintain as much information as possible when feature vectors aretransferred to next layer. The BP layer is trained to classify the features generated by the last RBM layer. Theexperiments are conducted on the Automatic Content Extraction 2004 dataset. This paper proves that acharacter-based feature is more suitable for Chinese relation extraction than a word-based feature. In addition, thepaper also performs a set of experiments to assess the Chinese relation extraction on different assumptions of anentity categorization feature. These experiments showed the comparison among models with correct entity types andimperfect entity type classified by DBN and without entity type. The results show that DBN is a successfulapproach in the high-dimensional-feature-space information extraction task. It outperforms state-of-the-art learningmodels such as SVM and back-propagation networks. |
| Key words: DBN (deep belief nets) neural network relation extraction deep architecture network character-based feature |
