Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet Allocation. Journal of Machine Learning Research 3, 993–1022 (2003)
Connan, J., Omlin, C.W.: Bibliography Extraction with Hidden Markov Models. Technical Report US-CS-TR-00-6, University of Stellenbosch (2000)
Chen, H., Branavan, S.R.K., Barzilay, R., Karger, D.R.: Global Models of Document Structure Using Latent Permutations. In: Proc. of North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL HLT) 2009 Conference, pp. 371–379 (2009)
Fligner, M.A., Verducci, J.S.: Distance Based Ranking Models. Journals of the Royal Statistical Society B 48(3), 359–369 (1986)
Hetzner, E.: A Simple Method for Citation Metadata Extraction Using Hidden Markov Models. In: Proc. of the 8th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 280–284 (2008)
Kramer, M., Kaprykowsky, H., Keysers, D., Breuel, T.M.: Bibliographic Meta-Data Extraction Using Probabilistic Finite State Transducers. In: Proc. of the 9th International Conference on Document Analysis and Recognition, pp. 609–613 (2007)
Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In: Proc. of the Eighteenth International Conference on Machine Learning, pp. 282–289 (2001)
Masada, T., Shibata, Y., Oguri, K.: Unsupervised Segmentation of Bibliographic Elements with Latent Permutations. International Journal of Organizational and Collective Intelligence 2(2), 49–62 (2011)
Sharifi, M.: Semi-supervised Extraction of Entity Attributes Using Topic Models. Master’s Thesis, Carnegie Mellon University (2009)
Takasu, A.: Bibliographic Attribute Extraction from Erroneous References Based on a Statistical Model. In: Proc. of the 3rd ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 49–60 (2003)
Yin, P., Zhang, M., Deng, Z.-H., Yang, D.-Q.: Metadata Extraction from Bibliographies Using Bigram HMM. In: Proc. of the 7th International Conference on Asian Digital Libraries, pp. 1–14 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Masada, T., Takasu, A., Shibata, Y., Oguri, K. (2011). Semi-supervised Bibliographic Element Segmentation with Latent Permutations. In: Xing, C., Crestani, F., Rauber, A. (eds) Digital Libraries: For Cultural Heritage, Knowledge Dissemination, and Future Creation. ICADL 2011. Lecture Notes in Computer Science, vol 7008. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24826-9_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-24826-9_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24825-2
Online ISBN: 978-3-642-24826-9
eBook Packages: Computer ScienceComputer Science (R0)