摘要
【目的】探究不同深度学习模型的科技论文摘要语步识别效果,并分析识别效果差异原因。【方法】构建大规模的科技论文结构化摘要语料库,选择10000和50000两种样本量的训练集,以传统机器学习方法SVM作为对比基准,引入多种深度学习方法(包括DNN、LSTM、Attention-BiLSTM等神经网络模型),开展语步识别实验,并对实验结果进行对比分析。【结果】Attention-BiLSTM方法在两种样本量下的实验中都取得最好的识别效果,50000样本量下F1值达0.9375;SVM方法的识别效果意外好于DNN、LSTM两种深度学习方法;但是,样本量从10000增加到50000时,SVM方法的识别效果提升最小(F1值提升0.0125),LSTM方法效果提升最大(F1值提升0.1125)。【局限】由于该领域尚未有公开的通用语料,主要以笔者收集的结构化论文摘要作为训练和测试语料,因此本文的研究结果在与他人比较时有一定的局限性。【结论】双向LSTM网络结构和注意力机制能够显著提升深度学习模型的语步识别效果;深度学习方法在大规模训练集下更能体现其优越性。
[Objective]This paper compares the performance of move recognition methods with different deep learning algorithms.[Methods]Firstly,we built a large training corpus.Then,we used the traditional machine learning method SVM as a benchmark,and developed four moves recognition models based on DNN,LSTM,Attention-Bi LSTM and LSTM.Finally,we conducted two rounds of experiments with sample size of 10,000 and 50,000.[Results]Attention-Bi LSTM method achieved the best results in both experiments over the four methods(F1=0.9375 with the larger sample).SVM method outperformed DNN and LSTM in both experiments.While changing sample size from 10,000 to 50,000,SVM received the least increase of F1 score(0.0125),and LSTM had the largest increase of F1 score(0.1125).[Limitations]There is no universal test corpus for similar research.Therefore,our results could not be compared with the results of other studies.[Conclusions]The bi-directional LSTM network structure and attention mechanism can significantly improve the performance of move recognition.The deep learning methods work better with larger sample size.
作者
张智雄
刘欢
丁良萍
吴朋民
于改红
Zhang Zhixiong;Liu Huan;Ding Liangping;Wu Pengmin;Yu Gaihong(National Science Library,Chinese Academy of Sciences,Beijing 100190,China;Department of Library Information and Archives Management,University of Chinese Academy of Sciences,Beijing 100190,China;Wuhan Library,Chinese Academy of Sciences,Wuhan 430071,China;Hubei Key Laboratory of Big Data in Science and Technology,Wuhan 430071,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2019年第12期1-9,共9页
Data Analysis and Knowledge Discovery
基金
中国科学院文献情报能力建设专项子项目“科技文献丰富语义检索应用示范”(项目编号:院1734)的研究成果之一.
关键词
深度学习
神经网络
语步识别
支持向量机
Deep Learning
Neural Network
Moves Recognition
Support Vector Machine
