@inproceedings{oai:nagasaki-u.repo.nii.ac.jp:00012513,
 author = {Masada, Tomonari and Takasu, Atsuhiro and Shibata, Yuichiro and Oguri, Kiyoshi},
 book = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
 month = {},
 note = {This paper proposes a semi-supervised bibliographic element segmentation. Our input data is a large scale set of bibliographic references each given as an unsegmented sequence of word tokens. Our problem is to segment each reference into bibliographic elements, e.g. authors, title, journal, pages, etc. We solve this problem with an LDA-like topic model by assigning each word token to a topic so that the word tokens assigned to the same topic refer to the same bibliographic element. Topic assignments should satisfy contiguity constraint, i.e., the constraint that the word tokens assigned to the same topic should be contiguous. Therefore, we proposed a topic model in our preceding work [8] based on the topic model devised by Chen et al. [3]. Our model extends LDA and realizes unsupervised topic assignments satisfying contiguity constraint. The main contribution of this paper is the proposal of a semi-supervised learning for our proposed model. We assume that at most one third of word tokens are already labeled. In addition, we assume that a few percent of the labels may be incorrect. The experiment showed that our semi-supervised learning improved the unsupervised learning by a large margin and achieved an over 90% segmentation accuracy., 13th International Conference on Asia-Pacific Digital Libraries, ICADL 2011; Beijing; 24 October 2011 through 27 October 2011, Lecture Notes in Computer Science, 7008, pp.60-69; 2011},
 pages = {60--69},
 publisher = {Springer Verlag},
 title = {Semi-supervised bibliographic element segmentation with latent permutations},
 volume = {7008},
 year = {2011}
}