The reviewed article presents the project BILBO and illustrates the application of several appropriate machine-learning techniques to the constitution of proper reference corpora and the construction of efficient annotation models. In this way, solutions are proposed for the problem of extracting and processing useful information from bibliographic references in digital documentation whatever their bibliographic styles are. It proves the usefulness and high degree of accuracy of CRF techniques, which involve finding the most effective set of features (including three types of features: input, local and global features) of a given corpus of well-structured bibliographical data (with labels such as surname, forename or title). Moreover, this approach has not only been proven efficient when applied to such traditional, well-structured bibliographical data sets, but it also originally contributes to the processing of more complicated, less-structured references such as the ones contained in footnotes by applying SVM with new features for sequence classification.
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