基于深度学习模型的摘要结构功能识别方法研究  

Research on the Recognition Method of Abstract Structure Function Based on Deep Learning Model

作　　者：刘忠宝 王宇飞[2] 张志剑 LIU Zhong—bao;WANG Yu-fei;ZHANG Zhi-jian(Institute of Language Intelligence,Beijing Language and Culture University,Beijing 100083,China;School of Software,North University of China,Taiyuan 030051,China)

机构地区：[1]北京语言大学语言智能研究院,北京100083 [2]中北大学软件学院,山西太原030051

出　　处：《情报科学》2021年第3期107-112,共6页Information Science

基　　金：国家社会科学基金一般项目“大数据环境下面向图书馆资源的跨媒体知识服务研究”(19BTQ012)。

摘　　要：【目的/意义】学术文献的摘要由目的、方法、结果等结构组成,这些结构具有特定的功能。目前,针对摘要功能结构识别的研究不多,且存在识别效率不高的问题,本文引入双向循环神经网络(Bidirectional Recurrent Neural Network, BiRNN)、双向长短时记忆网络(Bidirectional Long Short Term Memory, BiLSTM)、BiLSTM-CRF、BERT等深度学习模型,对1232篇情报类期刊论文进行摘要结构功能识别研究。【方法/过程】引入5折交叉验证法进行多次实验,以避免一次实验的偶然性;实验结果用"均值±标准差"形式表示,同时考虑模型的平均性能和稳定性;实验结果用F1值进行评价。【结果/结论】与BiRNN、BiLSTM、BiLSTM-CRF等模型相比,BERT模型具有最高的均值和最低的标准差,这表明该模型不仅具有最优的结构功能识别能力,而且性能稳定,该模型特别适用于摘要结构功能识别任务。【局限/创新】本文采用的实验语料规模较小且为人工标注,这限制了识别效率的提升。【Purpose/significance】The academic-literature abstract is composed of several structures with specific functions, such as purpose, method, result.【Method/process】There are few researches on the recognition methods of abstract structure function, and the proposed methods performed poor. In view of this, bidirectional recurrent neural network(RNN), bidirectional long short-term memory(BiLSTM), BiLSTM-CRF and bidirectional encoder representations from transformers(BERT) are introduced to summarize the journal articles of 1232 CNKI databases. In our experiments, The 5-fold cross validation is used to avoid contingency, the experiment results are represented by ’average ± standard deviation’, which takes the average performance and stability into consideration, the experiment results are evaluated by F1-value.【Result/conclusion】The comparative experiment results show that compared with BiRNN,BiLSTM, BiLSTM-CRF, BERT performs best with highest average and lowest standard deviation, which indicates that this model is quite fit for recognition of abstract structure function.【Innovation/limitation】The experimental corpus is small-scale and artificial-annotation, which limits the improvement of recognition performance.

关 键 词：结构功能识别 深度学习模型 双向循环神经网络 双向长短时记忆网络 条件随机场 BERT模型 

分 类 号：G254[文化科学—图书馆学]