Abstract
In this paper, we proposed an improved Hidden Markov Model (HMM) to extract metadata in the academic literatures. We have built a dataset including 458 literatures from the VLDB conferences, which contains the visual feature of text blocks. Our approach is based on the assumption that the text blocks in the same line have the same state (information type). The assumption is effective in more than 98% occasions. Thus, the state transition probability among the same states in the same line is much larger than that in different lines. According to this conclusion, we add one state transition matrix for HMM and modified the Viterbi algorithm. The experiments show that our extraction accuracy is superior to that of any existing works.
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Cui, BG., Chen, X. (2010). An Improved Hidden Markov Model for Literature Metadata Extraction. In: Huang, DS., Zhao, Z., Bevilacqua, V., Figueroa, J.C. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Lecture Notes in Computer Science, vol 6215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14922-1_26
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DOI: https://doi.org/10.1007/978-3-642-14922-1_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14921-4
Online ISBN: 978-3-642-14922-1
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