Identifying Important Citations by Incorporating Generative Model into Discriminative Classifiers
Authors: 
Xin Sun, Xin An, Shuo Xu, Liyuan Hao, Jinghong LiAuthors Info & Claims
Xin Sun, Xin An, Shuo Xu, Liyuan Hao, Jinghong LiAuthors Info & ClaimsPages 72 - 76
Abstract
Since Budapest open access initiative was launched, a large number of full-text articles in the format of XML are available, which further promotes the technology management on the basis of citation context analysis, such as emerging technology forecasting, technology opportunity detection and innovation measurement. Inspired by the success of kernel functions utilized to promote the performance of SVM (Support Vector Machine) model, we explore the potential of combining generative and discriminative models for the task of citation function and importance classification. In more details, generative features are generated from a topic model, Citation Influence Model (CIM), and then fed to two state-of-the-art discriminative models, SVM and RF (Random Forest), with other 13 features derived from citation contexts directly to identify important citations from a brand new perspective. Extensive experimental results on a dataset from the Association for Computational Linguistics anthology indicate that our approach outperforms the counterparts.
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August 2020
120 pages
ISBN:9781450375467
DOI:10.1145/3416028
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- Southwest Jiaotong University
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Published: 21 September 2020
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- Social Science Foundation of Beijing Municipality
- Fundamental Research Funds for the Central Universities
- Natural Science Foundation of Guangdong Province
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IMMS 2020
IMMS 2020: 2020 3rd International Conference on Information Management and Management Science
August 7 - 9, 2020
London, United Kingdom
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