Statistical Analysis of Bibliographic Strings for Constructing an Integrated Document Space
ECDL '02 Proceedings of the 6th European Conference on Research and Advanced Technology for Digital Libraries
Bibliographic attribute extraction from erroneous references based on a statistical model
Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries
Crf-based authors' name tagging for scanned documents
Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries
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We present an AI approach for the semantic recognition of bibliography references. The objective is to produce for each reference (given by an OCR flow), a structured data containing the list of the different sub-fields recognized and semantically validated. The validation is operated according to a Bibliography reference database, by the exam of principal terms in each reference field. The system uses an emergent architecture containing a Concept Network built from the database. This net represents the principal fields of the references and includes statistics on the occurrence of their terms. Validation is achieved dynamically by activation at each time of the more pertinent concepts. These concepts verify the presence of their terms by the execution of appropriate agents. This architecture is robust and non-deterministic allowing to find a solution in spite of OCR errors.