关键词: 嵌套实体关系抽取, 信息抽取, 支持向量机, 卷积神经网络

Abstract:

Nested named entities relationship extraction research lacks corresponding benchmark corpora. To solve this problem, manual annotation with machine learning are combined to extract their semantic relationships from an existing Chinese named entity recognition corpus. The authors manually annotate a Chinese nested named entity relation corpus from existing Chinese named entity recognition and conduct experiments with relation extraction between nested named entities via support vector machines (SVM) and convolutional neural network (CNN) models respectively. The experimental results show that the nested entity relation extraction performs excellently on the corpus with manually labeled entities, obtaining an F1 score of over 95%, while it falls short of expectations with automatically recognized entities.

Key words: nested entity relation extraction, information extraction, support vector machines, convolutional neural network