Abstract
A large amount of research papers are published in various fields and the ability to accurately extract metadata from a list of references is becoming increasingly important. Moreover, metadata extraction is crucial for measuring the influence of a particular study or researcher. However, it is difficult to automatically extract data from most lists of references because they consist of unstructured strings with bibliographies structured in various formats depending on the proceedings. Thus, this paper presents an effective and accurate method for extracting metadata, such as author name, title, publication year, volume, issue, page numbers, and journal name from heterogeneous references using the conditional random fields model. To conduct an experiment measuring the effectiveness of the proposed model, 1,415 references from 93 different academic papers published in Korea were used and a high accuracy of 97.10% was obtained.
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References
Day, M.Y., et al.: A knowledge-based approach to citation extraction. In: IRI-2005 IEEE International Conference on Information Reuse and Integration, Conference, pp. 50–55 (2005)
Hetzner, E.: A simple method for citation metadata extraction using Hidden Markov models. In: Proceedings of the 8th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 280–284. ACM (2008)
Ojokoh, B., Zhang, M., Tang, J.: A trigram Hidden MARKOV model for metadata extraction from heterogeneous references. Inf. Sci. 181(9), 1538–1551 (2011)
Gao, L., Tang, Z., Lin, X.: CEBBIP: a parser of bibliographic information in chinese electronic books. In: Proceedings of the 9th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 73–76. ACM (2009)
Tkaczyk, D., Szostek, P., Dendek, P.J., Fedoryszak, M., Bolikowski, L.: Cermine–automatic extraction of metadata and references from scientific literature. In: 2014 11th IAPR International Workshop on Document Analysis Systems (DAS), pp. 217–221. IEEE (2014)
Cortez, E., da Silva, A.S., Gonçalves, M.A., Mesquita, F., de Moura, E.S.: FLUX-CIM: flexible unsupervised extraction of citation metadata. In: Proceedings of the 7th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 215–224. ACM (2007)
Guo, Z., Jin, H.: Reference metadata extraction from scientific papers. In: 2011 12th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), pp. 45–49. IEEE (2011)
Tang, X., Zeng, Q., Cui, T., Wu, Z.: Regular expression-based reference metadata extraction from the web. In: 2010 IEEE 2nd Symposium on Web Society (SWS), pp. 346–350. IEEE (2010)
Lafferty, J., McCallum, A., Pereira, F.C.: Conditional random fields: probabilistic models for segmenting and labeling sequence data (2001)
Sun, W., Rumshisky, A., Uzuner, O.: Evaluating temporal relations in clinical text: 2012 i2b2 challenge. J. Am. Med. Inform. Assoc. 20(5), 806–813 (2013)
Yu, J., Fan, X.: Metadata extraction from Chinese research papers based on conditional random fields. In: FSKD, pp. 497–501. IEEE (2007)
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This research was supported by Korea Institute of Science and Technology Information (KISTI).
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Seol, JW., Choi, WJ., Jeong, HS., Hwang, HK., Yoon, HM. (2018). Reference Metadata Extraction from Korean Research Papers. In: Groza, A., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2018. Lecture Notes in Computer Science(), vol 11308. Springer, Cham. https://doi.org/10.1007/978-3-030-05918-7_5
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DOI: https://doi.org/10.1007/978-3-030-05918-7_5
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