CEBBIP: a parser of bibliographic information in chinese electronic books

ABSTRACT

Bibliographic information is essential for many digital library applications, such as citation analysis, academic searching and topic discovery. And bibliographic data extraction has attracted a great deal of attention in recent years. In this paper, we address the problem of automatic extraction of bibliographic data in Chinese electronic book and propose a tool called CEBBIP* for the task, which includes three main systems: data preprocessing, data parsing and data postprocessing. In the data preprocessing system, the tool adopts a rules-based method to locate citation data in a book and to segment citation data into citation strings of individual referencing literature. And a learning-based approach, Conditional Random Fields (CRF), is employed to parse citation strings in the data parsing system. Finally, the tool takes advantage of document intrinsic local format consistency to enhance citation data segmentation and parsing through clustering techniques. CEBBIP has been used in a commercial E-book production system. Experimental results show that CEBBIP's precision rate is very high. More specially, adopting the document intrinsic local format consistency obviously improves the citation data segmenting and parsing accuracy.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.7 Digital Libraries

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

General Terms:
Algorithms

Keywords:
bibliography, chinese electronic book, digital library, machine learning, metadata extraction