Application of SVM in citation information extraction

Title: Application of SVM in citation information extraction
Creator: Liang, Jiguang; Layton, Robert; Wang, Wei
Date: 2011
Type: Text; Conference proceedings
Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/43306
Identifier: vital:4556
Identifier: https://doi.org/10.1109/IWCDM.2011.15
Abstract: Support Vector Machines are an effective form of binary-class classification algorithm. To enhance the utilization of text structural features for information extraction, which are greatly restricted by the Hidden Markov Model (HMM), this paper proposes a support vector machine multi-class classification based on Markov properties to extract the information from a citation database. The proposed model extracts symbol characteristics as features and composes a binary tree of the transition probabilities. Experiments show that the proposed method outperforms HMM and basic SVM methods. © 2011 IEEE.
Publisher: Nanjing, Jiangsu
Rights: IEEE
Rights: This metadata is freely available under a CCO license
Subject: Classification; Feature extraction; Probability; Support Vector Machine (SVM); Symbol feature; Citation information; Classification algorithm; Information Extraction; Markov property; Multi-class classification; Structural feature; Support vector; Transition probabilities; Binary trees; Hidden Markov models; Text processing; Data mining

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