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Frontiers of Electrical and Electronic Engineering

ISSN 2095-2732

ISSN 2095-2740(Online)

CN 10-1028/TM

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Front Elect Electr Eng    2012, Vol. 7 Issue (2) : 242-253    https://doi.org/10.1007/s11460-011-0174-7
RESEARCH ARTICLE
Can prior knowledge help graph-based methods for keyword extraction?
Zhiyuan LIU(), Maosong SUN
Department of Computer Science and Technology, State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China
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Abstract

Graph-based methods are one of the widely used unsupervised approaches for keyword extraction. In this approach, words are linked according to their co-occurrences within the document. Afterwards, graph-based ranking algorithms are used to rank words and those with the highest scores are selected as keywords. Although graph-based methods are effective for keyword extraction, they rank words merely based on word graph topology. In fact, we have various prior knowledge to identify how likely the words are keywords. The knowledge of words may be frequency-based, position-based, or semantic-based. In this paper, we propose to incorporate prior knowledge with graph-based methods for keyword extraction and investigate the contributions of the prior knowledge. Experiments reveal that prior knowledge can significantly improve the performance of graph-based keyword extraction. Moreover, by combining prior knowledge with neighborhood knowledge, in experiments we achieve the best results compared to previous graph-based methods.
Keywords keyword extraction      prior knowledge      PageRank      DiffusionRank     
Corresponding Author(s): LIU Zhiyuan,Email:lzy.thu@gmail.com   
Issue Date: 05 June 2012
 Cite this article:   
Zhiyuan LIU,Maosong SUN. Can prior knowledge help graph-based methods for keyword extraction?[J]. Front Elect Electr Eng, 2012, 7(2): 242-253.
 URL:  
https://academic.hep.com.cn/fee/EN/10.1007/s11460-011-0174-7
https://academic.hep.com.cn/fee/EN/Y2012/V7/I2/242
Fig.1  Performance of Biased-PageRank on precision, recall, and F-measure with different damping factor (trust ratio =1.0) and different trust ratio (damping factor =0.85). The number of extracted keywords is set to =10. (a) Precision versus (=1.0); (b) recall versus (=1.0); (c) F-measure versus (=1.0); (d) precision versus (=0.85); (e) recall versus (=0.85); (f) F-measure versus (=0.85)
Fig.2  Performance of DiffusionRank on precision, recall, and F-measure versus different trust ratio . The number of extracted keywords =10 and the diffusion factor =0.5 and 2.0. (a) Precision (=0.5); (b) recall (=0.5); (c) F-measure (=0.5); (d) precision (=2.0); (e) recall (=2.0); (f) F-measure (=2.0)
Fig.3  Performance of DiffusionRank with measure on precision, recall, and F-measure with different numbers of extracted keywords. The trust ratio =0.1 and the diffusion factor =0.5
methodcorrectprecisionrecallF-measure
PageRank7440.2430.3000.269
Biased-PageRank7910.2590.3190.286
DiffusionRank7930.2590.3200.287
Tab.1  Results of PageRank and DiffusionRank on total correct extracted keywords, precision, recall, and F-measure when the number of extracted keywords is =10
methodcorrectprecisionrecallF-measure
PageRank8180.2680.3300.296
Biased-PageRank8810.2880.3550.318
DiffusionRank8850.2900.3570.320
Tab.2  Results of PageRank and DiffusionRank with neighborhood knowledge on total number of correct extracted keywords, precision, recall, and F-measure when the number of extracted keywords is =10
Fig.4  Performance of PageRank and DiffusionRank using neighborhood knowledge on precision, recall, and F-measure versus different numbers of extracted keywords. Here, the number of nearest neighbors is set =5. For DiffusionRank, the prior knowledge is calculated using and the trust ratio =0.1 and diffusion factor =0.5. (a) precision; (b) recall; (c) F-measure
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