基于PDF版式特征的文献篇章结构细粒度抽取方法研究

doi:10.13998/j.cnki.issn1002-1248.21-0237

Abstract

Abstract: [Purpose/Significance] This paper proposes a fine-grained automatic extraction method for document structure based on PDF layout features, in order to realize fine-grained organization of literature resources and meet the increasingly growing needs of users for accurate information services. [Method/Process] The method takes full advantage of machine learning in information classification, which can automatically analyze, identify and extract the chapter title of unstructured PDF documents based on layout features. And according to the coordinate positioning of chapter titles, the body content is automatically matched to the subordinated position of the title with paragraph as the minimum granularity, and the fine-grained extraction and identification of the full text of the document is finally realized. [Results/Conclusions] The test result shows that the average accuracy of automatic extraction can reach 80%. The method of fine-grained extraction of unstructured PDF documents proposed has practical significance and application prospect, and the data processing system designed based on the underlying method has been put into practical application, which will greatly liberate us from the mechanical drudgery of chapter structure extraction tasks.

Key words: layout features, chapter structure, chapter title, fine-grained extraction, machine learning

CLC Number:

G250

ZHAO Wanjing, LIU Minjuan, LIU Hongbing, WANG Xin, DUAN Feihu. A Fine-grained Extraction Method of Chapter Structure of Documents Based on PDF Layout Features[J].Journal of Library and Information Science in Agriculture, 2021, 33(9): 93-103.

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A Fine-grained Extraction Method of Chapter Structure of Documents Based on PDF Layout Features

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