Annotation and Joint Extraction of Scientific Entities and Relationships in NSFC Project Texts

Zhiyuan GE, Xiaoxi QI, Fei WANG, Tingli LIU, Jun GUAN, Xiaohong HUANG, Yong SHAO, Yingmin WU
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Abstract

Aiming at the lack of classification and good standard corpus in the task of joint entity and relationship extraction in the current Chinese academic field, this paper builds a dataset in management science that can be used for joint entity and relationship extraction, and establishes a deep learning model to extract entity and relationship information from scientific texts. With the definition of entity and relation classification, we build a Chinese scientific text corpus dataset based on the abstract texts of projects funded by the National Natural Science Foundation of China (NSFC) in 2018-2019. By combining the word2vec features with the clue word feature which is a kind of special style in scientific documents, we establish a joint entity relationship extraction model based on the BiLSTM-CNN-CRF model for scientific information extraction. The dataset we constructed contains 13060 entities (not duplicated) and 9728 entity relation labels. In terms of entity prediction effect, the accuracy rate of the constructed model reaches 69.15%, the recall rate reaches 61.03%, and the F1 value reaches 64.83%. In terms of relationship prediction effect, the accuracy rate is higher than that of entity prediction, which reflects the effectiveness of the input mixed features and the integration of local features with CNN layer in the model.

国家自然科学基金项目文本中科学实体与科学关系的标注与联合抽取
针对目前中国学术领域在联合实体和关系提取任务中缺乏分类和良好标准语料库的问题,本文构建了管理科学领域可用于联合实体和关系提取的数据集,并建立了从科学文本中提取实体和关系信息的深度学习模型。在实体定义和关系分类的基础上,以2018-2019年度国家自然科学基金资助项目的摘要文本为基础,构建了中文科学文本语料库数据集。将word2vec特征与科学文献中一种特殊样式的线索词特征相结合,建立了一种基于BiLSTM-CNN-CRF模型的联合实体关系抽取模型,用于科学信息抽取。我们构建的数据集包含13060个实体(不重复)和9728个实体关系标签。在实体预测效果方面,构建的模型准确率达到69.15%,召回率达到61.03%,F1值达到64.83%。在关系预测效果上,准确率高于实体预测,反映了模型中输入混合特征和局部特征与CNN层融合的有效性。
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