Unsupervised Segmentation of Bibliographic Elements with Latent Permutations


This paper introduces a novel approach for large-scale unsupervised segmentation of bibliographic elements. Our problem is to segment a word token sequence representing a citation into subsequences each corresponding to a different bibliographic element, e.g. authors, paper title, journal name, publication year, etc. Obviously, each bibliographic element… (More)
DOI: 10.1007/978-3-642-24396-7_20


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