| 实体-属性抽取的GRU+CRF方法 |
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| 引用本文： | 王仁武,孟现茹,孔琦.实体-属性抽取的GRU+CRF方法[J].现代情报,2018,38(10):57-64. |
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| 作者姓名： | 王仁武 孟现茹 孔琦 |
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| 作者单位： | 华东师范大学经济与管理学部信息管理系, 上海 200241 |
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| 基金项目： | 国家社会科学基金项目"基于数据驱动的图书馆资源发现系统平台研究"（项目编号：16BTQ026）。 |
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| 摘 要： | 目的/意义]研究利用深度学习的循环神经网络GRU结合条件随机场CRF对标注的中文文本序列进行预测，来抽取在线评论文本中的实体-属性。方法/过程]首先根据设计好的文本序列标注规范，对评论语料分词后进行实体及其属性的命名实体标注，得到单词序列、词性序列和标注序列；然后将单词序列、词性序列转为分布式词向量表示并用于GRU循环神经网络的输入；最后输出层采用条件随机场CRF，输出标签即是实体或属性。结果/结论]实验结果表明，本文的方法将实体-属性抽取简化为命名实体标注，并利用深度学习的GRU捕获输入数据的上下文语义以及条件随机场CRF获取输出标签的前后关系，比传统的基于规则或一般的机器学习方法具有较大的应用优势。
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| 关 键 词： | 实体属性抽取 GRU 循环神经网络 条件随机场 命名实体识别 |
Entity-Attribute Extraction with GRU+CRF Method |
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| Authors: | Wang Renwu Meng Xianru Kong Qi |
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| Institution: | Department of Information Management, Faculty of Economics and Management, East China Normal University, Shanghai 200241, China |
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| Abstract: | Purpose/Significance]The study used the recurrent neural network GRU combined conditional random field CRF to predict the annotated Chinese sequence text to extract the entity-attribute in the online review text.Method/Process]Firstly,according to the designed annotation specification to a text sequence,the paper made name entity annotations for entities and their attributes after the segmentation of corpus,and got word sequence,part of speech sequence and annotation sequence;Then the word sequence and part-of-speech sequence were converted into distributed word vector representation and used for input of GRU recurrent neural network;finally,the output layer used the conditional random field CRF and the output label was the entity or attribute.Result/Conclusion]The method in this paper simplified entity-attribute extraction to named entity annotation,and used GRU to capture the contextual semantics of input data and conditional random field CRF to obtain the output label context,which had a larger application advantage than the traditional rule based or general machine learning method. |
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| Keywords: | entity attribute extraction GRU RNN CRF NER |
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