基于LSTM模型的中文图书多标签分类研究  

Multi-Label Classification of Chinese Books with LSTM Model

作　　者：邓三鸿[1,2] 傅余洋子[1,2] 王昊 Deng Sanhong Fu Yuyangzi Wang Hao(School of Information Management, Nanjing University, Nanjing 210023 Jiangsu Key Laboratory of Data Engineering and Knowledge Service （Nanjing University）, Nanjing 210023, China)

机构地区：[1]南京大学信息管理学院,南京210023 [2]江苏省数据工程与知识服务重点实验室(南京大学),南京210023

出　　处：《数据分析与知识发现》2017年第7期52-60,共9页Data Analysis and Knowledge Discovery

基　　金：国家自然科学基金项目"面向学术资源的TSD与TDC测度及分析研究"(项目编号:71503121);中央高校基本科研业务费重点项目"我国图书情报学科知识结构及演化动态研究"(项目编号:20620140645)的研究成果之一

摘　　要：【目的】利用LSTM模型和字嵌入的方法构建分类系统,提出一种中文图书分类中多标签分类的解决方案。【方法】引入深度学习算法,利用字嵌入方法和LSTM模型构建分类系统,对题名、主题词等字段组成的字符串进行学习以训练模型,并采用构建多个二元分类器的方法解决多标签分类问题,选择3所高校5个类别的书目数据进行实验。【结果】从整体准确率、各类别精度、召回率、F1值多个指标进行分析,本文提出的模型均有良好表现,有较强的实际应用价值。【局限】数据仅涉及中图分类法5个类别,考虑的分类粒度较粗等。【结论】基于LSTM模型的中文图书分类系统具有预处理简单、增量学习、可迁移性高等优点,具备可行性和实用性。[Objective] This paper proposes a new method to automatically cataloguing Chinese books based on LSTM model, aiming to solve the issues facing single or multi-label classification. [Methods] First, we introduced deep learning algorithms to construct a new classification system with character embedding technique. Then, we trained the LSTM model with strings consisting of titles and keywords. Finally, we constructed multiple binary classifiers, which were examined with bibliographic data from three universities. [Results] The proposed model performed well and had practical value. [Limitations] We only analyzed five categories of Chinese bibliographies, and the granularity of classification was coarse. [Conclusions] The proposed Chinese book classification system based on LSTM model could preprocess data and learn incrementally, which could be transferred to other fields.

关 键 词：LSTM模型 深度学习 字嵌入 图书自动分类 多标签分类 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP391.1[自动化与计算机技术—控制科学与工程]