本文選題：LSTM模型 + 深度學習�。� 參考：《數(shù)據(jù)分析與知識發(fā)現(xiàn)》2017年07期

【摘要】：【目的】利用LSTM模型和字嵌入的方法構建分類系統(tǒng),提出一種中文圖書分類中多標簽分類的解決方案�！痉椒ā恳肷疃葘W習算法,利用字嵌入方法和LSTM模型構建分類系統(tǒng),對題名、主題詞等字段組成的字符串進行學習以訓練模型,并采用構建多個二元分類器的方法解決多標簽分類問題,選擇3所高校5個類別的書目數(shù)據(jù)進行實驗�！窘Y果】從整體準確率、各類別精度、召回率、F1值多個指標進行分析,本文提出的模型均有良好表現(xiàn),有較強的實際應用價值。【局限】數(shù)據(jù)僅涉及中圖分類法5個類別,考慮的分類粒度較粗等。【結論】基于LSTM模型的中文圖書分類系統(tǒng)具有預處理簡單、增量學習、可遷移性高等優(yōu)點,具備可行性和實用性。
[Abstract]:[objective] to construct a classification system by using LSTM model and word embedding method, and to put forward a solution of multi-label classification in Chinese book classification. [methods] an in-depth learning algorithm is introduced, and a classification system is constructed by word embedding method and LSTM model. In order to train the model, we use the method of constructing multiple binary classifiers to solve the problem of multi-label classification. The bibliographic data of five categories of three colleges and universities are selected to carry on the experiment. [results] from the overall accuracy, the precision of each category, the recall rate and the F1 value, the model presented in this paper has good performance. It has strong practical application value. [limitation] data only involve 5 categories of middle graph classification, and consider the classification granularity is coarser. [conclusion] the Chinese book classification system based on LSTM model has simple preprocessing and incremental learning. It has the advantages of high mobility, feasibility and practicability.
【作者單位】： 南京大學信息管理學院;江蘇省數(shù)據(jù)工程與知識服務重點實驗室(南京大學);
【基金】：國家自然科學基金項目“面向?qū)W術資源的TSD與TDC測度及分析研究”(項目編號:71503121) 中央高�；究蒲袠I(yè)務費重點項目“我國圖書情報學科知識結構及演化動態(tài)研究”(項目編號:20620140645)的研究成果之一
【分類號】：TP181;TP391.1
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本文編號：1802892