| 面向摘要结构功能划分的模型性能比较研究 |
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| 引用本文： | 王东波,陆昊翔,周鑫,朱丹浩.面向摘要结构功能划分的模型性能比较研究[J].图书情报工作,2018,62(12):84-90. |
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| 作者姓名： | 王东波 陆昊翔 周鑫 朱丹浩 |
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| 作者单位： | 1. 南京农业大学信息科学技术学院 南京 210095;
2. 南京大学信息管理学院 南京 210093;
3. 南京大学计算机科学与技术系 南京 210093 |
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| 基金项目： | 本文系国家社会科学基金重大项目"情报学学科建设与情报工作未来发展路径研究"（项目编号：17ZDA291）研究成果之一。 |
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| 摘 要： | 目的/意义]摘要作为学术论文中能够简明扼要地说明研究目的、研究方法和最终结论的陈述部分,具有较高的探究价值和意义。方法/过程]选取长短期记忆网络（Long Short-Term Memory）、支持向量机（Support Vector Machine）、LSTM-CRF和CNN-CRF 4种模型,对3 672篇情报学领域的期刊论文进行摘要划分识别研究。结果/结论]长短期记忆网络模型识别F值最高为69.15%,LSTM-CRF神经网络模型最高F值为88.76%,RNN-CRF模型最高F值达到89.10%,支持向量机分类器分类宏观F值最高为72.04%。该实验结果对图书情报领域的学术论文结构功能划分实验模型选取有较高的参考价值。
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| 关 键 词： | 结构功能划分 条件随机场 长短期记忆网络 卷积神经网络 支持向量机 |
| 收稿时间： | 2017-12-16 |
A Comparative Study of Model Performances Facing Abstract Structure Function |
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| Wang Dongbo,Lu Haoxiang,Zhou Xin,Zhu Danhao.A Comparative Study of Model Performances Facing Abstract Structure Function[J].Library and Information Service,2018,62(12):84-90. |
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| Authors: | Wang Dongbo Lu Haoxiang Zhou Xin Zhu Danhao |
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| Institution: | 1. Colledge of Information Science and Technology, Nanjing Agricultural University, Nanjing 210095;
2. Department of Information Management, Nanjing University, Nanjing 210093;
3. Department of Computer Science and Technology, Nanjing University, Nanjing 210093 |
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| Abstract: | Purpose/significance] Abstract can explain concisely the research purposes, research methods and the final part of the statement, which is of high exploration value and significance.Method/process] In this paper, four short-term memory networks (long short-term memory, support vector machine, LSTM-CRF and CNN-CRF) were selected to summarize the journal articles of 3672 CNKI databases.Result/conclusion] The long-term memory network model identifies the highest F value of 69.15%, the maximum F value of LSTM-CRF neural network model is 88.76%, and the highest F value of RNN-CRF model is 89.10%. The highest support vector machine classifier classification macro F value is 72.04%. The experimental results have a high reference value for the selection of the experimental model of the functional structure of academic dissertation in the field of library and information science. |
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| Keywords: | structure function division condition random field long-term memory network convolutional neural network support vector machine |
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