基于ERNIE-BiGRU模型的摘要语步自动识别研究  

Move Recognition in Academic Abstract via ERNIE-BiGRU Model

作　　者：温浩[1] 何茜茹 王杰 乔晓东[2] 张鹏[3] WEN Hao;HE Qianru;WANG Jie;QIAO Xiaodong;ZHANG Peng(School of Information and Control Engineering,Xi'an University of Architecture and Technology,Xi'an,Shaanxi 710311,China;Beijing Wanfang Data Co.,Ltd,Beijing 100038,China;School of Arts,Xi'an University of Architecture and Technology,Xi'an,Shaanxi 710311,China)

机构地区：[1]西安建筑科技大学信息与控制工程学院,陕西西安710311 [2]北京万方数据股份有限公司,北京100038 [3]西安建筑科技大学艺术学院,陕西西安710311

出　　处：《中文信息学报》2022年第11期91-100,共10页Journal of Chinese Information Processing

基　　金：国家自然科学基金(71673213)。

摘　　要：学术文献的摘要是对文献主要内容的浓缩,摘要不同部分的语步具有不同的信息,语步的自动识别和抽取对于学术摘要的后续研究有着重要的应用价值,而目前语步识别的研究相对较少,并且相关算法的效果还需要提高。针对上述问题,该文提出了一种基于ERNIE-BiGRU模型的语步识别算法。该算法首先结合中文句法分析理论提出基于句法依存关系的多语步结构拆分法,对学术文献摘要多语步结构进行自动拆分,获得多个单语步结构;然后构建用于训练的单语步结构语料库,并利用知识增强语义表示预训练模型,训练出句子级词向量;最后将训练出的单语步结构词向量信息输入双向门限循环单元(BiGRU)进行摘要语步自动化识别,取得了良好的效果。实验结果表明,该算法具有较好的鲁棒性和较高的识别精度,在结构化和非结构化摘要上的识别准确率分别达到了96.57%和93.75%。The academic abstract summarizes key points in a research paper,with a series of moves conveying different information.The automatic recognition and extraction of moves could provide a valuable foundation for other tasks related with the academic abstract.This paper proposed a move recognition algorithm for academic abstract based on ERNIE-BiGRU model.Firstly,a multi-move structure splitting method based on dependency structure is proposed,identifying the multiple single-move structure in the academic abstract.Secondly,a single-move structure corpus is constructed,and a pre-training model of knowledge-enhanced semantic representation is employed to train sentence-level word vectors.Finally,the trained word vector with single move structure information is input into BiGRU for automatic recognition of moves.The experimental results show that the algorithm has good robustness and high recognition accuracy,achieving 96.57%and 93.75%recognition accuracy for structured and unstructured abstracts,respectively.

关 键 词：中文句法分析 多语步结构拆分 ERNIE-BiGRU模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]