【摘要】 实体关系分类作为自然语言处理的基础任务，对知识图谱、智能问答、语义网构建等任务都起到了非常关键的作用。文中构建了BERT-GRU-ATT模型，以进行中文实体关系分类。为消除中文分词歧义对实体关系分类的影响，引入预训练模型BERT作为嵌入层，以较好地获得汉字的上下文信息；再通过双向门控循环单元捕获实体在句子中的长距离依赖，通过自注意力机制加强对关系分类贡献明显的字的权重，从而获得较好的实体关系分类结果。为了丰富中文实体关系分类语料，将SemEval2010＿Task8英文实体关系评测语料翻译为中文¹),该模型在此翻译语料上取得了75.46%的F1值，说明了所提模型的有效性。此外，所提模型在SemEval2010-task8英文数据集上F1值达到了80.55%,证明该模型对英文语料具有一定的泛化能力。更多还原

【Abstract】 As the basic task of natural language processing, entity relations classification plays a critical role in tasks such as knowledge graphs, intelligent question answering, semantic web construction and so on.This paper constructs the BERT-GRU-ATT model to classify Chinese entity relations.In order to eliminate the influence of Chinese word segmentation ambiguity on entity relations classification, the pre-training model BERT(bi-directional encoder representations from transformers) is introduced as the embedding layer to better obtain the context information of Chinese characters.Then gate recurrent unit(GRU) is used to capture the long-distance dependence of entities in sentences and self-attention mechanism(ATT) is used to strengthen the weight of characters that contribute significantly to relations classification, so as to obtain better results of entity relations classification.In order to enlarge the Chinese entity relations classification corpus, we translate the SemEval2010＿Task8 English entity relations evaluation corpus into Chinese.The model achieves an F1 value of 75.46% on this translation corpus, which shows the effectiveness of the proposed model.In addition, the model achieves an F1 of 80.55%on the SemEval2010-Task8 English dataset, which proves that the model has certain generalization ability to English corpus.更多还原