Citation recognition for scientific publications in digital libraries
Identifieur interne : 000218 ( France/Analysis ); précédent : 000217; suivant : 000219Citation recognition for scientific publications in digital libraries
Auteurs : Dominique Besagni [États-Unis] ; Abdel Belaïd [France]Source :
Descripteurs français
- mix :
- Wicri :
- topic : Citation.
Abstract
In this paper, a method based on part-of-speech tagging (PoS) is used for bibliographic reference structure. This method operates on a roughly structured ASCII file, produced by OCR.. Because of the heterogeneity of the reference structure, the method acts in a bottom-up way, without an a priori model, gathering structural elements from basic tags to sub-fields and fields. Significant tags are first grouped in homogeneous classes according to their categories and then reduced in canonical forms corresponding to record fields: ``authors'', title, conference name, date, etc. Non labeled tokens are integrated in one or another field by either applying PoS correction rules or using a inter- or intra-field model generated from well-detected records. The designed prototype operates with a great satisfaction on different record layouts and character recognition qualities. Without manual intervention, 96.6% words are correctly attributed, and about 75,9% references are completely segmented from 2,575 references.
Url:
Affiliations:
- France, États-Unis
- Lorraine
- Nancy
- Institut national polytechnique de Lorraine, Université Nancy 2, Université de Lorraine
Links toward previous steps (curation, corpus...)
- to stream Hal, to step Corpus: 000166
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- to stream Hal, to step Checkpoint: 000140
- to stream Main, to step Merge: 001479
- to stream Main, to step Curation: 001429
- to stream Main, to step Exploration: 001429
- to stream France, to step Extraction: 000218
Links to Exploration step
Hal:inria-00100181Le document en format XML
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<front><div type="abstract" xml:lang="en">In this paper, a method based on part-of-speech tagging (PoS) is used for bibliographic reference structure. This method operates on a roughly structured ASCII file, produced by OCR.. Because of the heterogeneity of the reference structure, the method acts in a bottom-up way, without an a priori model, gathering structural elements from basic tags to sub-fields and fields. Significant tags are first grouped in homogeneous classes according to their categories and then reduced in canonical forms corresponding to record fields: ``authors'', title, conference name, date, etc. Non labeled tokens are integrated in one or another field by either applying PoS correction rules or using a inter- or intra-field model generated from well-detected records. The designed prototype operates with a great satisfaction on different record layouts and character recognition qualities. Without manual intervention, 96.6% words are correctly attributed, and about 75,9% references are completely segmented from 2,575 references.</div>
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